Content area
In today's digital landscape, information security has become a critical concern, with adversaries constantly seeking ways to breach systems and compromise sensitive data. Steganography, a technique for covert communication by embedding information within innocuous cover media, has emerged as a potent tool for safeguarding information. However, the rise of adversarial attacks poses a significant threat to the efficacy of steganography. This paper explores the current use and significance of quantifying the impact of adversarial attacks on steganography's information-hiding security. By analyzing the vulnerabilities introduced by such attacks, the aim is to enhance the robustness of steganographic methods and fortify digital communication against malicious intrusions. The research quantifies the impact of adversarial attacks on steganography's information security. Motivated by threats in digital communication, the objectives include creating an adversarial attack framework, selecting techniques, and establishing impact metrics. The methodology uniquely combines adversarial attacks with machine learning to simulate practical scenarios. Results, presented through statistical analyses, tables, and graphs, reveal trade-offs between security and payload capacity, with visual aids enhancing the clarity of experiments. The findings provide insights into adversarial threats, guiding practical improvements in information security. The paper concludes with discussions on results, comparisons, and recommendations for fortifying steganographic systems against adversarial threats.
Introduction
Steganography is the practice of hiding data or information [1, 2] a term derived from Johannes Trithemus's work “Steganographia” (1462–1516) and rooted in the Greek term for "concealed writing." This technique involves embedding a message within an innocuous carrier such as text, images, audio, or video over a communication channel to effectively obscure the message's existence. It allows for the secretive transfer of information in a way that is not easily recognizable, ensuring it goes undetected [3]. Steganography reduces the risk of attacks by necessitating breaches of both the capture device's security and the system's private data, thereby making such attempts more difficult. Additionally, it is typically more secure than cryptography, as the key is embedded within the template, eliminating the need for a separate key [4]. Steganography is a developing area of research aimed at enhancing information security systems [5].
Digital watermarking serves as a technique for ownership verification and authentication by embedding hidden information, known as the watermark, within the content, referred to as cover media [6, 7]. In light of technological advancements, the landscape is marred by evolving adversarial threats, necessitating a profound exploration of potential vulnerabilities, particularly concerning adversarial attacks. This study investigates the quantification of how adversarial attacks impact information hiding security within the domain of steganography.
The motivation for this investigation arises from the escalating risks within digital communication. Adversarial attacks pose formidable challenges to the protective mechanisms of steganographic methodologies designed for safeguarding sensitive information [8]. In response to the growing sophistication of cyber threats, there is a crucial need to empirically measure the quantitative impact of adversarial attacks on the effectiveness of steganographic techniques. This study seeks to fill this gap by offering insights into the complex interaction between adversarial attacks and information hiding security in the context of steganography.
This research presents three primary objectives. First, a comprehensive adversarial attack framework tailored to specific steganographic techniques is developed. Second, metrics are established to allow for the quantitative assessment of the impact of adversarial attacks on information security. Third, a distinctive experiment is conducted, integrating adversarial attacks with machine learning to replicate real-world scenarios. This experimental approach offers a novel perspective on potential threats faced by steganographic systems [9].
The scope of this study encompasses a thorough exploration of how adversarial attacks impact information hiding security, with a specific focus on steganographic techniques [10]. The significance of this research lies in addressing a literature gap by quantifying the consequences of adversarial attacks on the confidentiality, integrity, and availability of concealed information. Insights derived from this study contribute not only to understanding steganography's resilience against adversarial threats but also guide practical advancements in information security. In an era where safeguarding information confidentiality is paramount, this research addresses a critical aspect of securing sensitive data amidst evolving cyber threats [11].
In a world where digital communication is dominant, ensuring the confidentiality and integrity of transmitted information is crucial. The technique of steganography, which conceals data within seemingly innocuous cover media, has become essential for safeguarding sensitive information. However, as the threat landscape evolves, adversarial attacks targeting steganographic systems have emerged, posing significant challenges to information security. This study delves into the complex interaction between steganography and adversarial attacks, uncovering the vulnerabilities that compromise the efficacy of information hiding techniques. Through detailed analysis and experimentation, this research aims to elucidate the impact of such attacks on steganographic systems, paving the way for enhanced defense mechanisms and resilient security protocols in the digital realm.
To analyze this subject with greater accuracy and structure, the following research questions are framed to achieve the utmost results in this research exploration:
How do adversarial attacks impact information hiding security within steganography?
What is the significance of quantifying the consequences of adversarial attacks on the confidentiality, integrity, and availability of concealed information?
What insights can be derived from the study regarding steganography's resilience against adversarial threats?
How can machine learning enhance steganographic security, as discussed in the research paper?
What are the research gaps identified in existing studies related to adversarial attacks on steganographic systems?
The following sections of this paper are organized as follows. Section 2 offers a concise review of related steganographic research. Section 3 identifies research gaps present in existing studies. Section 4 outlines the methodology for implementing the proposed steganographic framework, detailing the mathematical representation of message encoding and decoding, along with pseudocode and flowcharts. Section 5 presents the analysis and comparison of experimental results regarding encoding and decoding times using various steganography methods. Section 6 provides concluding remarks. Lastly, Sect. 7 outlines the proposed feature work.
Literature review
Overview of steganography
In Information Security Steganography serves as a pivotal technique in information security, involving the art of concealing data within seemingly innocent carriers [12]. The primary goal of this technique is to ensure the secure transmission of sensitive information by embedding it within non-apparent contexts. By maintaining confidentiality and integrity, steganography adds a discreet layer of protection, making it challenging for unauthorized entities to intercept or manipulate concealed data [13]. Steganographic methods include embedding information in images, audio files, or other media, providing a covert channel for secure communication.
Adversarial attacks in information security
Adversarial attacks pose a continuous threat to information security by strategically exploiting vulnerabilities within security measures. Common types of adversarial attacks include: eavesdropping (interception): unauthorized access to communication channels to capture hidden information [14]. Forgery (manipulation): alteration of the steganographic content to distort the embedded information. Statistical attacks: exploiting patterns or statistical irregularities to detect the presence of hidden data. Covert-channel attacks: diverting the attention of security measures by introducing auxiliary information to mask the hidden content [15]. Understanding these adversarial strategies is crucial for developing robust countermeasures and fortifying the effectiveness of steganography in information security.
Previous research on adversarial attacks in steganography
A comprehensive survey on steganography techniques and their robustness against attacks: this work investigates various steganographic methods and assesses their robustness against different attack vectors. Insights are provided into the strengths and weaknesses of commonly employed steganographic techniques [16]. The study compares the efficacy of steganographic systems against adversarial attacks, considering factors such as detection rates and robustness. A comparative analysis is offered on the impact of different adversarial strategies on steganographic security [17]. The integration of machine learning is explored to enhance the security of steganographic systems against adversarial threats [18]. The potential of adaptive steganographic techniques in countering evolving adversarial strategies is also investigated.
This paper provides a comprehensive survey and analysis of current digital image steganography methods. Various techniques used for hiding information within digital images are explored, assessing their strengths, weaknesses, and detection vulnerabilities. Through meticulous examination, the aim is to offer insights into the state-of-the-art in digital image steganography, facilitating advancements in secure communication protocols [19]. The analysis indicates an exponential increase in data exchange over the Internet, highlighting the critical importance of data security during communication. Ensuring the confidentiality and integrity of data during transmission remains a paramount concern. Steganography involves the concealment of a message, whether audio, image, or video, within another media file. This technique is employed to safeguard sensitive information from unauthorized access and malicious attacks [20].
The paper explains the literature survey on steganography, defining it as the art of concealing information in a cover media in such a way that the presence of the information is unknown. Digital image steganography accomplishes the potential for protected communication that is crucial in most applications today. Steganography offers several beneficial applications [21]. The development of information technology has led to a significant increase in the share of multimedia traffic in data networks. This necessitates addressing information security tasks related to multimedia data: protection against leakage of confidential information, identifying the source of the leak, ensuring the impossibility of unauthorized changes, and copyright protection for digital objects [22]. This paper considers applying two steganographic proposals (StegHash and SocialStegDisc) for a new distributed communication system by fulfilling assumptions of the cyberfog security approach. The initial design of such a system is proposed. Features and limitations were analyzed to prepare recommendations for further development and research [23]. This study demonstrates the significant advantages of utilizing the Fortran Matrix Laboratory (MatLab) programming language for steganography, particularly due to its ability to access memory spaces via pointers, thereby optimizing memory management and enhancing program execution efficiency. Fortran, being a programming language oriented to mathematical calculation in general, offers tools that facilitate its application, especially in the matrix calculation used in steganography [24]. This study presents the combination of triple data encryption standard (3-DES) and least significant bit (LSB) to improve the security measures applied to medical data. The Java programming language was employed to develop a simulation program for the experiment. The results indicate that medical data can be stored, shared, and managed in a reliable and secure manner using the combined model [25]
As per Table 1, the following studies conducted studies can be concluded based on literature review of research are as follow:
Table 1. Comparative analysis of specific features for the different studies
Research study | Focus | Methodology | Key findings | References |
|---|---|---|---|---|
Study 1: "A Comprehensive Survey on Steganography Techniques" | Analysis of robustness in steganography against attack vectors | Examination of various steganographic methods Evaluation of robustness against eavesdropping, forgery, statistical attacks, and cover channel attacks | Identification of strengths and weaknesses in different steganographic techniques Insights into vulnerabilities to specific attack vectors | Kadhim et al. (2019) [26] |
Study 2: "Adversarial Attacks on Steganographic Systems" | Comparative analysis of steganographic systems | Comparative evaluation of multiple steganographic systems Assessment of detection rates and robustness against adversarial attacks | Comparative insights into the effectiveness of different steganographic systems Understanding the impact of adversarial strategies on system security | Li et al. (2021) [27] |
Study 3: "Machine Learning-Based Approaches to Enhance Security" | Integration of machine learning in steganography | Exploration of machine learning techniques to enhance steganographic security Investigation of adaptive steganographic techniques Demonstration of the potential of machine learning in fortifying steganographic systems | Development of adaptive techniques to counter evolving adversarial threats | Shang et al. (2020) [18] |
Study 4: "Quantum Steganography: Exploiting Quantum Properties for Secure Information Hiding | Investigation of quantum steganography leveraging principles from quantum mechanics | Findings of quantum steganography | Theoretical analysis and simulation studies exploring the potential advantages and challenges of quantum steganography in terms of security, robustness, and detection resistance | Min-Allah et al. (2022) [28] |
Study 5: "Digital Watermarking: Enhancing Data Security Through Imperceptible Embedded Signatures" | Evaluation of digital watermarking methodologies for data integrity and authenticity | Comprehensive review of digital watermarking techniques, including spatial and frequency domain methods, and their effectiveness in ensuring data security | Comparative analysis of watermarking algorithms shedding light on their resilience against various attacks and their applicability in diverse domains such as multimedia forensics and copyright protection | Cox et al. (2007) [29] |
Study 1, titled "A Comprehensive Survey on Steganography Techniques," provides a detailed analysis of the robustness of steganography against various attack vectors. Through meticulous examination of a diverse array of steganographic methods, the resilience of these techniques against eavesdropping, forgery, statistical attacks, and cover-channel attacks is evaluated. The findings offer valuable insights into the strengths and weaknesses inherent in different steganographic approaches, highlighting vulnerabilities to specific attack vectors.
Study 2, titled "Adversarial Attacks on Steganographic Systems," conducts a comparative analysis of multiple steganographic systems, focusing on assessing detection rates and robustness against adversarial attacks. By subjecting these systems to rigorous scrutiny, the research aims to gain comparative insights into their effectiveness and susceptibility to adversarial manipulation. Understanding the impact of adversarial strategies on system security is crucial for devising countermeasures to mitigate potential threats.
Study 3, titled "Machine Learning-Based Approaches to Enhance Security," explores the integration of machine learning techniques in steganography to bolster security. The research investigates how machine learning can enhance steganographic security and explores adaptive steganographic techniques. By demonstrating the potential of machine learning in fortifying steganographic systems, the study emphasizes the development of adaptive techniques capable of countering evolving adversarial threats. This study underscores the importance of developing adaptive strategies that could safeguard sensitive information in an increasingly hostile digital landscape.
Study 4, titled "Quantum Steganography: Exploiting Quantum Properties for Secure Information Hiding," delves into the emerging field of quantum steganography, which leverages principles from quantum mechanics to hide information within quantum data. Through theoretical analysis and simulation studies, the research examines the potential advantages and challenges of quantum steganography in terms of security, robustness, and detection resistance. Insights gained from this study could pave the way for novel approaches to information hiding that utilize the unique properties of quantum systems.
Study 5, titled "Digital Watermarking: Enhancing Data Security Through Imperceptible Embedded Signatures," reviews digital watermarking techniques as an alternative approach to steganography for protecting digital content through imperceptible embedded signatures. This study provides a comprehensive review of digital watermarking methodologies, including spatial and frequency domain techniques, and evaluates their effectiveness in ensuring data integrity and authenticity. Comparative analysis of watermarking algorithms sheds light on their resilience against various attacks and their applicability in diverse domains such as multimedia forensics and copyright protection.
Research gaps in existing studies
Many studies concentrate on specific attack vectors in controlled environments, which may overlook broader threat landscapes presented in real-world scenarios
The absence of standardized metrics for quantifying the impact of adversarial attacks hinders comprehensive comparisons across different steganographic techniques, making it challenging to draw universal conclusions.
The exploration of adaptive steganographic techniques capable of dynamically countering adversarial threats remains underexplored in existing literature.
Some studies focus primarily on specific media types (images, audio), which could limit the generalizability of findings across diverse steganographic applications [30].
Addressing these limitations is vital for advancing the field, ensuring a comprehensive understanding of vulnerabilities, and developing effective countermeasures against a diverse range of adversarial threats in steganography [31].
Presents a comparative analysis of the proposed work, highlighting key metrics and benchmarks against existing methods in Table 2. This comparison provides insights into the performance, efficiency, and effectiveness of the proposed approach relative to established techniques.
Table 2. Comparative analysis of proposed work
Study | Focus | Methodology | Key findings | Our novel contributions | References |
|---|---|---|---|---|---|
Enhancing Steganography's Resilience | Robustness of steganographic methods | Evaluation against statistical attacks | Identified vulnerabilities in existing methods | Introduced machine learning to enhance robustness against adaptive attacks | Shang et al. (2020) [18] |
Deep Learning for Robust Steganography | Steganography using deep learning | Implementation of CNN for steganographic security | Demonstrated improved security using deep learning | Combined adversarial attack framework with machine learning for real-world scenarios | Li et al. (2021) [32] |
Steganography Techniques: A Comparative Analysis | Comparative analysis of steganographic techniques | Comparative evaluation of detection rates | Provided insights into strengths and weaknesses of various techniques | Developed a quantitative assessment framework for adversarial impact | Liu et al. (2019) [33] |
Crypto-Steganography: Enhanced Security | Enhancing security of steganographic systems | Integration of cryptographic techniques | Showcased enhanced security through combined cryptographic methods | Proposed a novel adversarial attack framework specific to steganographic techniques | Obaid et al. (2021) [34] |
Secure Medical Data with Steganography | Steganography in medical data | Application of LSB and 3-DES in medical data security | Improved security measures for medical data | Experimentally validated the impact of adversarial attacks on LSB-based methods | Singh et al. (2020) [35] |
Implementation methodology
Steganography involves various techniques, and different methods possess distinct formulas. However, a basic representation of the process can be expressed mathematically. For instance, LSB (least significant bit) substitution serves as a simple method for hiding information in an image [36]. The encoding and decoding formulas in steganography employ various mathematical equations and algorithms, depending on the specific technique utilized. Common tools for encoding and decoding in steganography include programming languages such as Python and MATLAB, along with specialized software packages like StegExpose, OpenStego, and Steghide.
Dataset source and utilization
The dataset used in this research study has been meticulously collected from various online sources, drawing upon the extensive repositories of digital information available on the internet. Through systematic methods, datasets relevant to the research objectives were identified and gathered from publicly accessible databases, repositories, academic archives, and open data portals. Selection criteria prioritized datasets based on relevance, quality, comprehensiveness, and metadata availability, ensuring the robustness of analyses and findings. Rigorous quality assurance measures were implemented to verify data authenticity and ensure accuracy and consistency. Ethical considerations were paramount throughout the collection process, with strict adherence to data privacy regulations and ethical standards. Acknowledgment is extended to the creators and contributors of the datasets for their invaluable efforts in data collection and dissemination. Researchers often utilize standard benchmark datasets, such as the BOSS dataset or the BOWS-2 dataset [37], containing a diverse range of cover images and corresponding stego images created using various steganographic techniques.
Description of attack
The type of adversarial attack utilized in this research is a perturbation attack. This attack involves the introduction of Gaussian noise onto the stego image created through steganography. The impact is significant as it disrupts the integrity and confidentiality of the concealed message. By manipulating the pixel values within the stego image, this perturbation attack aims to obscure the hidden information while maintaining the visual appearance of the image. Consequently, the presence of the concealed data becomes harder to detect, posing a threat to the security of digital communication channels.
This perturbation attack not only undermines the robustness of traditional steganographic methods but also highlights potential vulnerabilities in information-hiding techniques. Its effectiveness lies in its ability to introduce uncertainty and ambiguity, thereby complicating the extraction of hidden information. As a result, the perturbation attack serves as a noteworthy avenue for exploring the resilience of steganographic systems against adversarial threats in contemporary digital environments.
Mathematical representation
Encoding formula
Let C(x,y) be the pixel value at coordinates (x,y) in the cover image, and Si be the ith bit of the secret message.
The encoding process involves substituting the least significant bit (LSB) of each pixel value with the corresponding bit from the secret message.
1
This formula adjusts the LSB of each pixel to embed the secret message.
Decoding formula
Let O(x,y) be the pixel value at coordinates (x,y) in the output image.
The decoding process involves extracting the least significant bit (LSB) of each pixel value to reconstruct the secret message.
2
This formula retrieves the LSB of each pixel, forming the secret message.
Figure 1 illustrates the methodological framework used to evaluate steganography and adversarial attacks, outlining the key steps and processes involved. The framework encompasses the evaluation of steganographic methods and their resilience against adversarial attacks, with particular emphasis on the RGB (red, green, and blue) color channels in image analysis.
[See PDF for image]
Fig. 1
Methodological framework for evaluating steganography and adversarial attacks (RGB: red, green and blue)
Methodologies for simulating adversarial attacks on steganography
Steganography-based information-hiding systems face increasing threats from adversaries seeking to compromise their security. To simulate and analyze adversarial attacks in this context, a variety of methodologies and approaches have been employed. These include machine learning-based techniques, statistical analyses, and experimentation with real-world datasets. Machine learning algorithms, such as convolutional neural networks (CNNs) and generative adversarial networks (GANs), have been utilized to generate adversarial examples that exploit vulnerabilities in steganographic systems. Statistical analyses involve examining patterns and irregularities in stego images to detect the presence of hidden data and assess the robustness of steganographic techniques. Experimentation often involves creating controlled environments to evaluate the effectiveness of adversarial attacks under different conditions, such as varying payload sizes or types of cover media. By employing these methodologies, researchers aim to gain insights into the vulnerabilities of steganography-based systems and develop countermeasures to enhance their security.
Categorization and evaluation of adversarial attacks in steganography
In the methodology used to study adversarial attacks on steganography-based information-hiding systems, different types of attacks are defined and categorized based on their characteristics and objectives. Common types of adversarial attacks include eavesdropping (interception), forgery (manipulation), statistical attacks, and covert-channel attacks. These attacks are evaluated for their effectiveness based on criteria such as detection rates, robustness against detection techniques, and impact on the security of concealed information. Evaluation metrics include false positive rates, false negative rates, and overall accuracy in detecting hidden data. By categorizing and evaluating adversarial attacks, researchers can better understand their capabilities and devise strategies to mitigate their impact on steganographic systems.
Experimental setup for assessing adversarial attacks on steganography security
The experimental setup used to assess the impact of adversarial attacks on the security of steganography techniques is crucial for obtaining meaningful results. This setup typically involves several components, including datasets, steganography algorithms, and evaluation metrics. Steganography algorithms under investigation may include LSB embedding, spatial domain techniques, or frequency domain techniques [38]. Evaluation metrics commonly used to assess the security of steganography systems against adversarial attacks include detection rates, bit error rates, and payload capacity. By carefully designing the experimental setup, researchers can systematically evaluate the effectiveness of adversarial attacks and measure their impact on steganography security.
Characterization and generation of adversarial attacks in steganography
Adversarial attacks in the context of steganography are defined and characterized based on their objectives, techniques, and impact on security. These attacks are often generated or simulated using various methods, including mathematical models, machine learning algorithms, and statistical analyses. For instance, adversarial examples can be generated using optimization techniques to find perturbations that maximize the likelihood of misclassification by steganalysis algorithms. Alternatively, statistical analyses may be employed to identify patterns or anomalies in stego images that indicate the presence of hidden data. Adversarial attacks may also be categorized based on their level of sophistication, ranging from simple manipulation of LSB embedding to more complex techniques involving machine learning-based evasion strategies. By characterizing and generating adversarial attacks, researchers can explore the vulnerabilities of steganography systems and develop defenses against potential threats [39].
Limitations and assumptions in studying adversarial attacks in steganography
In studying adversarial attacks in steganography, certain limitations and assumptions are encountered that shape the scope and interpretation of the findings. These limitations may include constraints on computational resources, access to datasets, or the complexity of steganography algorithms under investigation. Assumptions might be made regarding the capabilities and intentions of adversaries, the nature of cover media, or the availability of side-channel information. Additionally, trade-offs between realism and controlled experimentation may need to be considered when designing adversarial attack scenarios. By acknowledging and addressing these limitations and assumptions, researchers can enhance the validity and applicability of their research findings in the context of steganography security [40].
Quantitative evaluation of adversarial attack effectiveness
The effectiveness of adversarial attacks on steganography systems is quantitatively measured or evaluated using various metrics and criteria. These metrics include detection rates, false positive rates, false negative rates, and overall accuracy in detecting hidden data. Detection rates measure the percentage of stego images correctly identified as containing hidden information, while false positive and false negative rates quantify the frequency of misclassifications. Overall accuracy provides a comprehensive measure of the effectiveness of adversarial attacks in evading detection mechanisms. By quantitatively evaluating the impact of adversarial attacks, researchers can assess the robustness of steganography techniques and identify areas for improvement in their security defenses.
Metrics for evaluating steganography technique security
The security and robustness of steganography techniques against adversarial attacks are evaluated using various metrics and criteria. These metrics may include detection rates, false positive rates, false negative rates, and payload capacity. Detection rates measure the percentage of stego images correctly identified as containing hidden information, while false positive and false negative rates quantify the frequency of misclassifications. Payload capacity indicates the maximum amount of data that can be concealed within a cover image without detection. Additionally, metrics such as distortion measures and visual quality assessments may be used to evaluate the perceptual quality of stego images. By employing these metrics, researchers can assess the security and robustness of steganography techniques and identify areas for improvement in their defense mechanisms against adversarial attacks.
Analytical techniques for evaluating adversarial attack impact
Statistical and analytical techniques are applied to analyze the data collected from experiments assessing the impact of adversarial attacks on steganography security. These techniques may include hypothesis testing, regression analysis, and machine learning algorithms for pattern recognition. The results are analyzed to identify trends, correlations, and statistical significance in the effectiveness of adversarial attacks. Additionally, qualitative analysis methods, such as content analysis or thematic coding, may be utilized to interpret textual or visual data. By applying analytical techniques, insights can be gained into the impact of adversarial attacks on steganography security, leading to meaningful conclusions that inform future research and development efforts.
Proof of perturbation attack
The proof of the perturbation attack involves demonstrating its impact on the stego image generated by steganography. A comparison between the original stego image and the perturbed stego image can be made to observe the changes introduced by the attack. The following approaches may be taken to provide evidence of the attack:
Visual comparison: Both the original stego image and the perturbed stego image are displayed side by side, allowing for visual inspection of differences.
Analysis of pixel values: Both the original stego image and the perturbed stego image are displayed side by side, allowing for visual inspection of differences. Analysis of pixel values: statistical measures such as mean, standard deviation, and histogram of pixel values are computed for both images. By comparing these measures, the extent of perturbation introduced by the attack can be quantified.
Decoding accuracy: The effect of the perturbation on decoding accuracy is verified. If the perturbation significantly degrades decoding accuracy, this provides further evidence of the attack's effectiveness.
Result and discussion
Analyzing performance metrics in steganography
In this comprehensive analysis, the critical performance metrics of a steganographic system are examined, focusing on both encoding and decoding processes. This investigation provides valuable insights into the efficiency and effectiveness of the chosen steganographic method.
Encoding time analysis
The encoding time graph shown in Fig. 2, which illustrates the duration required to embed a secret message within a cover image using the selected steganographic technique. The x-axis represents individual encoding instances, while the y-axis indicates the time in seconds for each process. Upon examination, patterns or variations in the graph may reveal factors influencing encoding time, such as image size, message length, or the complexities of the steganographic method employed. This analysis serves to highlight the efficiency of the encoding process, offering practical implications for real-world applications.
[See PDF for image]
Fig. 2
Encoding time analysis: the x-axis represents individual encoding instances, while the y-axis indicates the time in seconds for each process
Decoding time analysis
Complementing the encoding analysis which can be observed in Fig. 3, the decoding time graph depicts the time required to extract a hidden message from an encoded image. The x-axis denotes individual decoding instances, and the y-axis represents the time in seconds for each process. Observations may include trends or variations in decoding time, providing a deeper understanding of factors affecting efficiency, such as image complexity or the chosen steganographic method. A comparative analysis with encoding times enriches this exploration, offering a holistic view of the steganographic system's overall performance. This examination of decoding time contributes to the evaluation of practical implications and potential limitations in real-world scenarios.
[See PDF for image]
Fig. 3
Decoding time analysis: the x-axis denotes individual decoding instances, and the y-axis represents the time in seconds for each process
Visualizing the transformation
Before and after steganography in this exploration, The visual impact of steganography is explored through a comparative analysis of two images: one prior to the application of steganography and another after the encoding of a hidden message within it [41].
Before using steganography
The initial image Fig. 4, devoid of any steganographic alterations, is presented as a pristine representation of its original content. Each pixel retains the unaltered essence of the visual composition [42]. The colors, patterns, and details remain unmarred by any hidden information, providing a baseline for comparison. This "before" image functions as a canvas showcasing the unadulterated state of the visual medium, offering clarity and transparency.
[See PDF for image]
Fig. 4
Before using steganography
After encoding a message
In the Fig. 5, which illustrates The application of steganography induces a transformation in the secondary image, embedding a concealed message within the pixel matrix. Although the naked eye may struggle to discern any changes, meticulous analysis would reveal subtle alterations strategically applied to embed information seamlessly [43]. The steganographic technique, chosen for its subtlety, ensures that the visual integrity of the image remains intact while concealing a hidden message within its depths. The "after" image stands as a testament to the fusion of art and covert communication.
[See PDF for image]
Fig. 5
After encoding a message
In this visual journey, a stark contrast is explored between two images—one in its pristine, unaltered state, and the other transformed after the application of steganography. The "before" image serves as a testament to the untouched visual composition, showcasing the original colors, patterns, and details without any hidden information. In contrast, the "after" image, crafted through the intricacies of steganography, conceals a covert message within its pixels [44]. While minimal changes may be perceived by the naked eye, a closer inspection unravels the subtleties strategically applied to seamlessly embed information. Together, these visual narratives illustrate the transformative power of steganography, blending artistry and covert communication within the realm of digital imagery [45].
In the initial Fig. 6, which illustrates the exploration of comparing the original and coded images, a seemingly enigmatic result unfolded—the emergence of a completely black difference image. Contrary to expectations, this black canvas is not an anomaly but rather a deliberate representation conveying a profound message: there are no discernible differences between the two images.
[See PDF for image]
Fig. 6
Original, coded and difference images
The black difference image, in its visual silence, eloquently communicates that, at the pixel level, the original and coded images are indistinguishable. This absence of contrast implies that the steganographic encoding process successfully embedded the message within the cover image without perceptible alterations to the naked eye [46].
In the realm of image processing, a black difference image serves as a powerful indicator of the effectiveness of the steganographic technique employed [47]. It underscores the subtlety and seamless integration of the hidden message, rendering it virtually imperceptible in the visual domain.
This visual silence, while initially intriguing, speaks volumes about the artistry and efficacy of steganography, highlighting its capacity to conceal information within the intricate layers of an image without leaving visible traces [48]. The black difference image becomes a symbol of success, emphasizing the covert nature of the encoded message and the artful mastery of steganographic practices.
In the initial Fig. 7, which illustrates the pursuit of unraveling the intricacies of steganography, the second code has proven to be a powerful tool, offering a nuanced understanding of the differences between the original and coded images [49]. In RGB images, where each pixel comprises three channels (red, green, and blue, each ranging from 0 to 255, with a bit depth of 24 bits), the red, green, and blue channels are first extracted from the cover image. The least significant bits (LSBs) of one of these channels are then replaced with the bits of the covert message before recombining the three channels to create the stego image [50]. A novel steganography scheme for RGB images was proposed, offering high image quality and enhanced data-hiding capacity. This method employs a binary matrix and an integer weight matrix as secret keys for secure data concealment, with the weight matrix enhancing the data-hiding ratio [51]. By visualizing the RGB differences through a meticulously crafted graph, this code provides a comprehensive analysis that transcends the limitations of a singular grayscale representation.
[See PDF for image]
Fig. 7
RGB difference graph
The RGB difference graph allows for a detailed dissection of the variations in color channels—red, green, and blue—revealing the intricate interplay of hues within the steganography-encoded images. Each channel's histogram unfolds a unique narrative, shedding light on the specific color components influenced by the steganographic process [52].
Analyzing the RGB differences graph not only exposes subtle variations but also elucidates the collaborative harmony of colors within the images. Peaks and troughs in the histograms symbolize areas where the original and coded images diverge or align, guiding toward a deeper comprehension of the steganographic transformation.
This graphical exploration delves beyond visual aesthetics, offering a quantitative perspective on the effectiveness of the steganographic technique employed. The interwoven spectrum of RGB differences emerges as a canvas painted with insights into the covert communication concealed within the pixels of the encoded image.
As the RGB differences are navigated through, this code unfolds a rich tapestry of information, elevating the understanding of steganography's impact on color dynamics. It invites contemplation on the seamless integration of hidden messages within the visual spectrum, transcending grayscale limitations and paving the way for more comprehensive analyses in the realm of digital concealment.
Conclusion
In this research, a thorough examination was conducted to assess the vulnerabilities and impacts of adversarial attacks on steganographic systems. By developing a comprehensive adversarial attack framework integrated with machine learning techniques, the effects of these attacks on the security of information hiding were quantified. The findings reveal critical trade-offs between security and payload capacity, offering valuable insights into the resilience of various steganographic methods against adversarial threats. Through the simulation of real-world scenarios, it was demonstrated that steganographic techniques are highly susceptible to perturbation attacks, which significantly compromise the confidentiality and integrity of concealed information. These results underscore the importance of developing robust steganographic frameworks capable of withstanding evolving adversarial strategies. Furthermore, the exploration of steganography, supported by detailed visualizations and analyses, unveiled the intricate balance between the art of concealment and the technical processes underlying it. The encoding and decoding time analyses provided critical insights into the efficiency and practical implications of steganographic processes. Visualizing the transformation of digital imagery from an unaltered state to a covertly encoded form highlighted the delicate interplay between visual fidelity and data concealment. The analysis of RGB colour channels further enriched the understanding, revealing how steganographic transformations subtly alter the spectrum of hues within an image. However, the challenge of adversarial attacks remains a significant concern. These attacks threaten the robustness and security of steganographic techniques, necessitating ongoing research to fortify these methods against potential threats. Understanding the vulnerabilities associated with adversarial attacks is essential for advancing the resilience of steganographic processes. In conclusion, this study has expanded the body of knowledge regarding digital concealment while emphasizing the need for continuous innovation to stay ahead of adversarial challenges. Ensuring the continued efficacy and security of steganographic practices in an ever-evolving digital landscape is of paramount importance.
This research has achieved several significant milestones that contribute to the field of steganography and its resilience against adversarial attacks. Notably, the development of a novel adversarial attack framework tailored specifically to steganographic techniques enables a precise assessment of vulnerabilities, enhancing the understanding of weaknesses in current steganographic methods. Additionally, the establishment of quantitative metrics for evaluating the impact of adversarial attacks on information security provides a standardized approach for comparing the robustness of different steganographic techniques, which is crucial for assessing the effectiveness of various countermeasures. Finally, the integration of machine learning techniques has enhanced the robustness of steganographic systems, introducing adaptive mechanisms.
Future research directions and challenges
Future research efforts should focus on enhancing steganographic security through a multifaceted approach, incorporating advanced encryption algorithms from post-quantum cryptography and machine learning techniques for real-time anomaly detection. The application of dynamic embedding techniques, tailored to image characteristics and environmental factors, may significantly improve the resilience of steganographic methods. Expanding the scope of steganography to encompass multimedia formats, such as videos and audio files, offers the potential for more robust concealment methods. Integrating steganography into secure communication platforms and digital watermarking systems could facilitate covert communication and enhance copyright protection. The consideration of ethical implications and adherence to regulatory guidelines are essential for the responsible use of steganography, ensuring both security and trust in covert communications while addressing potential risks. Steganography is expected to continue its evolution, with several key directions and challenges likely to shape its future. The incorporation of post-quantum cryptography is recommended to fortify the security of steganographic methods, particularly in anticipation of quantum computing advancements that might compromise current encryption standards. Expanding steganographic techniques to include a broader range of media, such as audio, video, and text, could increase their applicability and enhance resilience across various formats. Practical implementation of steganographic systems in secure communication and data protection is necessary to ensure that theoretical advancements translate into functional technologies. A significant challenge involves developing adaptive steganographic methods capable of dynamically responding to evolving adversarial strategies; the integration of AI and machine learning might aid in creating systems capable of countering sophisticated attacks.
Additionally, there is a need to establish universally accepted metrics and benchmarks for evaluating the effectiveness of steganographic techniques, especially in the context of adversarial attacks, which would enable more consistent and rigorous assessments. As steganography advances, it is crucial to address its ethical and legal implications, ensuring responsible use and compliance with laws to maintain trust and prevent misuse.
Declarations
Conflict of interest
I declare that I have no conflict of interest in relation to this work.
References
1. Basahel, AM; Yamin, M; Abi Sen, AA. Enhancing security of transmitted data by improved steganography method. IJcSNS; 2019; 19,
2. Vidhya H, Shilpa N, Parvin SM. Secured speech data hiding using steganography of images
3. Chaudhary S, Dave M, Sanghi A (2016) Text steganography based on feature coding method. In: Proceedings of the international conference on advances in information communication technology & computing, pp 1–4
4. Kant, C; Nath, R; Chaudhary, S. Biometrics security using steganography. Int J Secur; 2008; 2,
5. Tyagi, S; Dwivedi, RK; Saxena, AK. A novel PDF steganography optimized using segmentation technique. Int J Inf Technol; 2020; 12,
6. Sarbavidya S, Karforma S. Security of smart-card based personal data using digital watermarking
7. Sarbavidya, S; Karforma, S. Uml implementation of e-tendering using secret key digital watermarking. Int J Comput Distrib Syst; 2012; 1,
8. Cai, Z; Xiong, Z; Xu, H; Wang, P; Li, W; Pan, Y. Generative adversarial networks: a survey toward private and secure applications. ACM Comput Surv (CSUR); 2021; 54,
9. Hassaballah, M; Hameed, MA; Awad, AI; Muhammad, K. A novel image steganography method for industrial internet of things security. IEEE Trans Ind Inf; 2021; 17,
10. Johnson, NF; Duric, Z; Jajodia, S. Information hiding: steganography and watermarking-attacks and countermeasures: steganography and watermarking: attacks and countermeasures; 2001; Berlin, Springer Science & Business Media: [DOI: https://dx.doi.org/10.1007/978-1-4615-4375-6] 0269.02034
11. Ghamizi S, Cordy M, Papadakis M, Le Traon Y (2021) Evasion attack steganography: turning vulnerability of machine learning to adversarial attacks into a real-world application. In: Proceedings of the IEEE/CVF international conference on computer vision, pp 31–40
12. Sumathi CP, Santanam T, Umamaheswari G (2014) A study of various steganographic techniques used for information hiding. arXiv:1401.5561
13. Mawla, NA; Khafaji, HK. Enhancing data security: a cutting-edge approach utilizing protein chains in cryptography and steganography. Computers; 2023; 12,
14. Zou, Y; Wang, G. Intercept behavior analysis of industrial wireless sensor networks in the presence of eavesdropping attack. IEEE Trans Ind Inf; 2015; 12,
15. Firoozjaei, MD; Jeong, JP; Ko, H; Kim, H. Security challenges with network functions virtualization. Futur Gener Comput Syst; 2017; 67, pp. 315-324. [DOI: https://dx.doi.org/10.1016/j.future.2016.07.002] 1173.60316
16. Shehab, DA; Alhaddad, MJ. Comprehensive survey of multimedia steganalysis: techniques, evaluations, and trends in future research. Symmetry; 2022; 14,
17. Tao, F; Cao, C; Li, H; Zou, B; Wang, L; Sun, J. Adversarial attack for deep steganography based on surrogate training and knowledge diffusion. Appl Sci; 2023; 13,
18. Shang, Y; Jiang, S; Ye, D; Huang, J. Enhancing the security of deep learning steganography via adversarial examples. Mathematics; 2020; 8,
19. Cheddad, A; Condell, J; Curran, K; Mc Kevitt, P. Digital image steganography: survey and analysis of current methods. Signal Process; 2010; 90,
20. Dhawan, S; Gupta, R. Analysis of various data security techniques of steganography: a survey. Inf Secur J Glob Perspect; 2021; 30,
21. Mandal, PC; Mukherjee, I; Paul, G; Chatterji, BN. Digital image steganography: a literature survey. Inf Sci; 2022; 609,
22. Evsutin, O; Melman, A; Meshcheryakov, R. Digital steganography and watermarking for digital images: a review of current research directions. IEEE Access; 2020; 8, pp. 166589-166611. [DOI: https://dx.doi.org/10.1109/ACCESS.2020.3022779] 1442.30007
23. Bieniasz J, Szczypiorski K (2018) Towards empowering cyber attack resiliency using steganography. In: 2018 4th international conference on frontiers of signal processing (ICFSP), Poitiers, pp 24–28
24. Serpa-Andrade L, Garcia-Velez R, Pinos-Velez E, Flores-Urgilez C (2021) Analysis of the application of steganography applied in the field of cybersecurity. In: Ahram TZ, Karwowski W, Kalra J (eds) Advances in artificial intelligence, software and systems engineering. AHFE 2021. Lecture Notes in Networks and Systems
25. Babatunde, AO et al. Information security in health care centre using cryptography and steganography. AZOJETE; 2018; 14,
26. Kadhim, IJ; Premaratne, P; Vial, PJ; Halloran, B. Comprehensive survey of image steganography: techniques, evaluations, and trends in future research. Neurocomputing; 2019; 335, pp. 299-326. [DOI: https://dx.doi.org/10.1016/j.neucom.2018.06.075] 0762.90048
27. Li, L; Fan, M; Liu, D. AdvSGAN: adversarial image steganography with adversarial networks. Multimed Tools Appl; 2021; 80,
28. Min-Allah, N; Nagy, N; Aljabri, M; Alkharraa, M; Alqahtani, M; Alghamdi, D; Alshaikh, R. Quantum image steganography schemes for data hiding: a survey. Appl Sci; 2022; 12,
29. Cox, I; Miller, M; Bloom, J; Fridrich, J; Kalker, T. Digital watermarking and steganography; 2007; Burlington, Morgan Kaufmann: 1185.68312
30. Kamil P, Masruroh S, Hakiem N, Simangunsong F, Bidari A (2020) Robustness analysis of a steganography file against a media sharing process in instant messaging applications. In: Proceedings of the 2nd international conference on Quran and hadith studies information technology and media in conjunction with the 1st international conference on Islam, science and technology, ICONQUHAS & ICONIST, Bandung, October 2–4, 2018, Indonesia
31. Alshamrani, A; Myneni, S; Chowdhary, A; Huang, D. A survey on advanced persistent threats: Techniques, solutions, challenges, and research opportunities. IEEE Commun Surv Tutor; 2019; 21,
32. Li, Q; Wang, X; Wang, X; Ma, B; Wang, C; Shi, Y. An encrypted coverless information hiding method based on generative models. Inf Sci; 2021; 553, pp. 19-30.4193051 [DOI: https://dx.doi.org/10.1016/j.ins.2020.12.002] 1486.94124
33. Liu, Y; Liu, S; Wang, Y; Zhao, H; Liu, S. Video steganography: a review. Neurocomputing; 2019; 335, pp. 238-250. [DOI: https://dx.doi.org/10.1016/j.neucom.2018.09.091] 1405.94122
34. Obaid, ZK; Al Saffar, NFH. Image encryption based on elliptic curve cryptosystem. Int J Electr Comput Eng; 2021; 11,
35. Singh, L; Singh, AK; Singh, PK. Secure data hiding techniques: a survey. Multimed Tools Appl; 2020; 79, pp. 15901-15921. [DOI: https://dx.doi.org/10.1007/s11042-018-6407-5] 1521.62481
36. Akhtar N, Johri P, Khan S (2013) Enhancing the security and quality of LSB based image steganography. In: 2013 5th international conference and computational intelligence and communication networks, pp 385–390. IEEE, pp 385–390
37. NCU, Ankita Gupta (2023) “BOWS2”, Mendeley Data, V1. https://doi.org/10.17632/kb3ngxfmjw.1
38. Shi H, Dong J, Wang W, Qian Y, Zhang X (2018) SSGAN: secure steganography based on generative adversarial networks. In: Advances in multimedia information processing–PCM 2017: 18th pacific-rim conference on multimedia, Harbin, September 28–29, 2017, Revised Selected Papers, Part I 18. Springer International Publishing, pp 534–544
39. Goodfellow IJ, Shlens J, Szegedy C (2015) Explaining and harnessing adversarial examples. In: Proceedings of the international conference on learning representations (ICLR)
40. Bender, W; Gruhl, D; Morimoto, N; Lu, A. Techniques for data hiding. IBM Syst J; 1996; 35,
41. How to communicate secretly with SteganoGAN?—Online. https://analyticsindiamag.com/how-to-communicate-secretly-with-steganogan/
42. Morkel T, Eloff JH, Olivier MS (2005) A reversible image steganographic algorithm based on Slantlet transform. In: ISSA, vol 1, no 2, pp 1–11
43. Anderson, RJ; Petitcolas, FA. On the limits of steganography. IEEE J Sel Areas Commun; 1998; 16,
44. Sallee P (2003) Model-based steganography. In: International workshop on digital watermarking. Springer, Berlin, pp 154–167
45. Muttoo, SK; Kumar, S. Data hiding in JPEG images. Int J Inf Technol (IJIT); 2009; 1, pp. 13-16.1203.82106
46. Bailey, K; Curran, K. An evaluation of image-based steganography methods. Multimed Tools Appl; 2006; 30, pp. 55-88. [DOI: https://dx.doi.org/10.1007/s11042-006-0008-4] 1117.05070
47. Muttoo SK, Kumar S (2009) Robust source coding steganographic technique using wavelet transforms. In: International Journal of Information Technology Bharati Vidyapeeth’s Institute of Computer Applications and Management (BVICAM), pp 91–96
48. Muttoo, SK; Kumar, S. A robust source coding watermark technique based on magnitude DFT decomposition. BIJIT BVICAM’s Int J Inf Technol; 2012; 4,
49. Kaur S, Bansal S, Bansal RK (2014) Steganography and classification of image steganography techniques. In: 2014 international conference on computing for sustainable global development (INDIACom). IEEE, pp 870–875
50. Rahman, S; Masood, F; Khan, WU; Ullah, N; Khan, FQ; Tsaramirsis, G; Ashraf, M. A novel approach of image steganography for secure communication based on LSB substitution technique. Comput Mater Continua; 2020; 64,
51. Kumar S, Muttoo SK. Image based steganography methods with high payload
52. Amirtharajan, R; Akila, R; Deepikachowdavarapu, P. A comparative analysis of image steganography. Int J Comput Appl; 2010; 2,
© Bharati Vidyapeeth's Institute of Computer Applications and Management 2024.