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New Approach on Steganalysis: Reverse-Engineering based Steganography SW Analysis
... H. Lee et al. [208] use reverse engineering on the "Steg" software (steganography software using the IDA tool) to propose countermeasures to detect and defeat this type of software. ...
... Further articles explore different aspects of the anti-forensic field, including the definition and classification of anti-forensic techniques [204], their impact on the investigation of cybercrime [16], their application in blogs to maintain anonymity [205], methods by which to hide data in NTFS partitions [206], the analysis of anti-forensic techniques in databases [207], and the reverse engineering of steganography tools [208], all of them focusing on the proposal of countermeasures to solve these problems. ...
... Among the contributions of these studies are the definition and classification of antiforensic techniques [204], the proposal of countermeasures for law enforcement [16], the analysis of anti-forensic techniques in blogs to maintain anonymity [205], methods by which to hide data in NTFS partitions [206], the analysis of techniques with which to prevent anti-forensic techniques in databases [207], and countermeasures for "Steg" software (steganography software using the IDA tool) [208]. ...
The main purpose of anti-forensic computer techniques, in the broadest sense, is to hinder the investigation of a computer attack by eliminating traces and preventing the collection of data contained in a computer system. Nowadays, cyber-attacks are becoming more and more frequent and sophisticated, so it is necessary to understand the techniques used by hackers to be able to carry out a correct forensic analysis leading to the identification of the perpetrators. Despite its importance, this is a poorly represented area in the scientific literature. The disparity of the existing works, together with the small number of articles, makes it challenging to find one’s way around the vast world of computer forensics. This article presents a comprehensive review of the existing scientific literature on anti-forensic techniques, mainly DFIR (digital forensics incident response), organizing the studies according to their subject matter and orientation. It also presents key ideas that contribute to the understanding of this field of forensic science and details the shortcomings identified after reviewing the state of the art.
... payload (referred to as active Steganalysis [59]) from pragmatic data either with or without the prior knowledge of the underneath Steganography algorithm and allied parameters. This technique has gained significant prominence in forensic sciences and attained state-level recognition [60] in technically advanced countries [61] because detection or unveiling of the concealed information may help avoid and overcome catastrophic security situations. Recent interest in digital Steganalysis dates to the publication of the report regarding illegal usage of Steganography by the malevolent engaged in terrorist activities [62], which further got intensified after the 9/11 calamity [63][64]. ...
The endangerment of online data breaches calls for exploring new and enhancing existing sneaky ways of clandestine communication to tailor those to match the present and futuristic technological and environmental needs, to which malicious intruders wouldn't have an answer. Cryptography and Steganography are the two distinct techniques that, for long, have remained priority choices for hiding vital information from the unauthorized. But the visibility of the encrypted contents makes these vulnerable to attack. Also, the recent legislative protection agreed to law enforcement authorities in Australia to sneak into pre-shared cryptographic secret keys (PSKs) shall have a devastating impact on the privacy of the people. Hence, the need of the hour is to veil in the encrypted data underneath the cover of Steganography, whose sole intent is to hide the very existence of information. This research endeavor enhances one of the most famous images Steganography technique called the Least Significant Bit (LSB) Steganography, from the security and information-theoretic standpoint by taking a known-cover and known-message attack scenario. The explicit proclamation of this research endeavor is that the security of LSB Steganography lies in inducing uncertainty at the time of bit embedding process. The test results rendered by the proposed methodology confers on the non-detectability and imperceptibility of the confidential information along with its strong resistance against LSB Steganalysis techniques.
Digital image steganography, the concealing of information within seemingly innocent photographs presents considerable hurdles for traditional detection methods. To address this, we provide a unique deep learning-based steganalysis model is designed to identify the hidden data in digital photos. The methods used to detect steganographic content accurately against a variety of steganographic strategies using convolutional neural networks (CNNs) and generative adversarial networks (GANs). Our model has a multi-stage architecture that includes modules for feature extraction, representation learning, and decision-making, all of which are meant to capture complicated patterns indicating steganographic modifications. We use cutting-edge CNN architectures like ResNet and DenseNet for feature extraction, allowing the model to detect tiny visual cues typical of steganographic embedding. Furthermore, to improve the model's capacity to generalize across diverse steganographic procedures and payloads, we incorporate GAN-based data augmentation approaches, allowing it to learn a more thorough representation of steganographic content variants. Experimental results show that our methodology is effective at recognizing steganographic content with high precision and recall rates, beating existing methods across a variety of parameters. Furthermore, we undertake extensive experiments to evaluate the model's resilience to adversarial attacks and capacity to extend to previously unknown steganographic techniques, confirming its robustness and practical usefulness in real-world contexts.
In the steganographic picture, it remains a challenging problem to determine the best place for inserting the hidden message with a minimum distortion of the host medium. However, there is a long way to go to select the right embedding position with less distortion. To accomplish this goal, we suggest a new high performance image steganography method in which extreme machine learning algorithms (ELM) are updated to build a supervised mathematical model. The ELM is initially checked in regression mode on a portion of the picture or host medium. This helped us to determine the best place to embed the message with the best values in the expected assessment measurements. For practicing on a new metric, contrast, homogeneity and other texture characteristics are used. In addition, the established ELM is used to tackle over fitting during workout. The findings are analyzed using the correlation, the structural similarity measure, fusion matrices and mean square error in the efficiency of the proposed steganography approach. In terms of imperceptibility, the adjusted ELM has been found to transcend current approaches. Excellent characteristics of the findings indicate that the proposed steganographic method is highly capable of retaining the visual image detail. In accordance with the current state of the art approaches, an increase of 28 per cent of imperceptibility is achieved.
The ability to reverse engineer a product has been important for as long as technology has existed. A vital activity in most branches of industrial design and production has been to acquire samples of the products sold by competing companies and pick them apart. Understanding the engineering done by your competing opponents can shed insight into the strengths and weaknesses of their products, reveal the engineering ideas behind their products’ features, and fertilize and further improve the innovation that goes on in one’s own company.
Steganalysis of high capacity Wavelet based fusion image steganography with encryption, using Image quality metrics (as a set of features) is proposed. As the first order image statistics using the proposed algorithm are inherently preserved, which is desirable feature of the scheme, improving the security of algorithm against the targeted attacks .In addition comparing the present steganography scheme with two different encryption techniques, on the undetectibility ground, the generalized objective metric like SVD is used as a steganalysis tool. DFrFT encryption is found statistically and visually undetectable achieving the desired robustness though PSNR values are better in DNA encryption
The eventual goal of steganalytic forensic is to extract the hidden messages embedded in steganographic images. A promising technique that addresses this problem partially is steganographic payload location, an approach to reveal the message bits, but not their logical order. It works by finding modified pixels, or residuals, as an artifact of the embedding process. This technique is successful against simple least-significant bit steganography and group-parity steganography. The actual messages, however, remain hidden as no logical order can be inferred from the located payload. This paper establishes an important result addressing this shortcoming: we show that the expected mean residuals contain enough information to logically order the located payload provided that the size of the payload in each stego image is not fixed. The located payload can be ordered as prescribed by the mean residuals to obtain the hidden messages without knowledge of the embedding key, exposing an inherent vulnerability in these embedding algorithms. Experimental results are provided to support our analysis.
Steganalysis and steganography are the two different sides of the same coin. Steganography tries to hide messages in plain sight while steganalysis tries to detect their existence or even more to retrieve the embedded data. Both steganography and steganalysis received a great deal of attention, especially from law enforcement. While cryptography in many countries is being outlawed or limited, cyber criminals or even terrorists are extensively using steganography to avoid being arrested with encrypted incriminating material in their possession. Therefore, understanding the ways that messages can be embedded in a digital medium –in most cases in digital images-, and knowledge of state of the art methods to detect hidden information, is essential in exposing criminal activity. Digital image steganography is growing in use and application. Many powerful and robust methods of steganography and steganalysis have been presented in the literature over the last few years. In this literature review, we will discuss and present various steganalysis techniques – from earlier ones to state of the art- used for detection of hidden data embedded in digital images using various steganography techniques.
What is steganography? Steganography, coming from the Greek words stegos, meaning roof or covered and graphia which means writing, is the art and science of hiding the fact that commu-nication is taking place. Using steganography, you can embed a secret message inside a piece of unsuspicious information and send it without anyone knowing of the existence of the secret message. Steganography and cryptography are closely related. Cryptography scrambles mes-sages so they cannot be understood. Steganography on the other hand, will hide the message so there is no knowledge of the existence of the message in the first place. In some situations, sending an encrypted message will arouse suspicion while an "invisible" message wil not do so. Both sciences can be combined to produce better protection of the message. In this case, when the steganography fails and the message can be detected, it is still of no use as it is encrypted using cryptography techniques. Therefore, the principle defined once by Kerckhoffs for cryptography, also stands for steganography: the quality of a cryptographic system should only depend on a small part of information, namely the secret key. The same is valid for good steganographic systems: knowledge of the system that is used, should not give any information about the existence of hidden messages. Finding a message should only be possible with knowledge of the key that is required to uncover it. New technology? Steganographic techniques have been used for centuries. The first known application dates back to the ancient Greek times, when messengers tattoed messages on their shaved heads and then let their hair grow so the message remained unseen. A different method from that time used wax tables as a cover source. Text was written on the underlying wood and the message was covered with a new wax layer. The tablets appeared to be blank so they passed inspection without question.
Steganography is the art of secret communication and steganalysis is the art of detecting the hidden messages embedded in digital media using steganography. Both steganography and steganalysis have received a great deal of attention from law enforcement and the media. In the past years many powerful and robust methods of steganography and steganalysis have been reported in the literature. In this paper, we classify and give an account of the various approaches that have been proposed for steganalysis. Some promising methods for statistical steganalysis have also been identified.
A Survey on LSB Based Steganography Methods
- Pavani M.
Steganalysis: Detecting hidden information with computer forensic analysis, SANS Institute Information Security Reading Room
- P Richer
- Richer P.