CNN Based Adversarial Embedding with Minimum Alteration for Image Steganography

Historically, steganographic schemes were designed in a way to preserve image statistics or steganalytic features. Since most of the state-of-the-art steganalytic methods employ a machine learning (ML) based classifier, it is reasonable to consider countering steganalysis by trying to fool the ML classifiers. However, simply applying perturbations on stego images as adversarial examples may lead to the failure of data extraction and introduce unexpected artefacts detectable by other classifiers. In this paper, we present a steganographic scheme with a novel operation called adversarial embedding, which achieves the goal of hiding a stego message while at the same time fooling a convolutional neural network (CNN) based steganalyzer. The proposed method works under the conventional framework of distortion minimization. Adversarial embedding is achieved by adjusting the costs of image element modifications according to the gradients backpropagated from the CNN classifier targeted by the attack. Therefore, modification direction has a higher probability to be the same as the sign of the gradient. In this way, the so called adversarial stego images are generated. Experiments demonstrate that the proposed steganographic scheme is secure against the targeted adversary-unaware steganalyzer. In addition, it deteriorates the performance of other adversary-aware steganalyzers opening the way to a new class of modern steganographic schemes capable to overcome powerful CNN-based steganalysis.Comment: Submitted to IEEE Transactions on Information Forensics and Securit

Statistical stegdetectors performance by message re-embedding

State-of-the-art stegdetectors for digital images are based on pre-processing (calibration) of analyzed image for increasing stego-to-cover ratio. In most cases, the calibration is realized by image processing with enormous set of high-pass filters to obtain good estimation of cover image from the stego one. Nevertheless, the efficiency of this approach significantly depends on careful selection of filters for reliably extraction of cover image alterations that are specific for each embedding method. The selection is non-trivial and laborious operation that is realized today by training of convolutional neural networks, such as Ye-Net, SR-Net to name but a few. The paper is devoted to performance analysis of alternative approach to image calibration, namely message re-embedding into analyzed image. The considered method is aimed to increasing stego-to-cover ratio by amplification of cover image alterations caused by message hiding. The analysis was performed on ALASKA and VISION datasets by usage of stegdetector based on SPAM model of covers. Messages were re-embedded according to state-of-the-art adaptive methods HUGO, S-UNIWARD, MG and MiPOD. Proposed approach allows significantly (up to 20%) decreasing detection error even in case of low payload of cover image (less than 10%) where modern stegdetectors are ineffective

Side-Information For Steganography Design And Detection

Today, the most secure steganographic schemes for digital images embed secret messages while minimizing a distortion function that describes the local complexity of the content. Distortion functions are heuristically designed to predict the modeling error, or in other words, how difficult it would be to detect a single change to the original image in any given area. This dissertation investigates how both the design and detection of such content-adaptive schemes can be improved with the use of side-information. We distinguish two types of side-information, public and private: Public side-information is available to the sender and at least in part also to anybody else who can observe the communication. Content complexity is a typical example of public side-information. While it is commonly used for steganography, it can also be used for detection. In this work, we propose a modification to the rich-model style feature sets in both spatial and JPEG domain to inform such feature sets of the content complexity. Private side-information is available only to the sender. The previous use of private side-information in steganography was very successful but limited to steganography in JPEG images. Also, the constructions were based on heuristic with little theoretical foundations. This work tries to remedy this deficiency by introducing a scheme that generalizes the previous approach to an arbitrary domain. We also put forward a theoretical investigation of how to incorporate side-information based on a model of images. Third, we propose to use a novel type of side-information in the form of multiple exposures for JPEG steganography

UNDERWATER COMMUNICATIONS WITH ACOUSTIC STEGANOGRAPHY: RECOVERY ANALYSIS AND MODELING

In the modern warfare environment, communication is a cornerstone of combat competence. However, the increasing threat of communications-denied environments highlights the need for communications systems with low probability of intercept and detection. This is doubly true in the subsurface environment, where communications and sonar systems can reveal the tactical location of platforms and capabilities, subverting their covert mission set. A steganographic communication scheme that leverages existing technologies and unexpected data carriers is a feasible means of increasing assurance of communications, even in denied environments. This research works toward a covert communication system by determining and comparing novel symbol recovery schemes to extract data from a signal transmitted under a steganographic technique and interfered with by a simulated underwater acoustic channel. We apply techniques for reliably extracting imperceptible information from unremarkable acoustic events robust to the variability of the hostile operating environment. The system is evaluated based on performance metrics, such as transmission rate and bit error rate, and we show that our scheme is sufficient to conduct covert communications through acoustic transmissions, though we do not solve the problems of synchronization or equalization.Lieutenant, United States NavyApproved for public release. Distribution is unlimited

JPEG Steganography and Synchronization of DCT Coefficients for a Given Development Pipeline

This short paper proposes to use the statistical analysis of the correlation between DCT coefficients to design a new synchronization strategy that can be used for cost-based steganographic schemes in the JPEG domain. First, an analysis is performed on the covariance matrix of DCT coefficients of neighboring blocks after a development similar to the one used to generate BossBase. This analysis exhibits groups of uncorrelated coefficients: 4 groups per block and 2 groups of uncorrelated diagonal neighbors together with groups of mutually correlated coefficients groups of 6 coefficients per blocs and 8 coefficients between 2 adjacent blocks. Using the uncorrelated groups, an embedding scheme can be designed using only 8 disjoint lattices. The cost map for each lattice is updated firstly by using an implicit underlying Gaussian distribution with a variance directly computed from the embedding costs, and secondly by deriving conditional distributions from multivariate distributions. The covariance matrix of these distributions takes into account both the correlations exhibited by the analysis of the covariance matrix and the variance derived from the cost. This synchronization scheme enables to obtain a gain of PE of 5% at QF 95 for an embedding rate close to 0.3 bnzac coefficient using DCTR feature sets

Data Hiding in Digital Video

With the rapid development of digital multimedia technologies, an old method which is called steganography has been sought to be a solution for data hiding applications such as digital watermarking and covert communication. Steganography is the art of secret communication using a cover signal, e.g., video, audio, image etc., whereas the counter-technique, detecting the existence of such as a channel through a statistically trained classifier, is called steganalysis. The state-of-the art data hiding algorithms utilize features; such as Discrete Cosine Transform (DCT) coefficients, pixel values, motion vectors etc., of the cover signal to convey the message to the receiver side. The goal of embedding algorithm is to maximize the number of bits sent to the decoder side (embedding capacity) with maximum robustness against attacks while keeping the perceptual and statistical distortions (security) low. Data Hiding schemes are characterized by these three conflicting requirements: security against steganalysis, robustness against channel associated and/or intentional distortions, and the capacity in terms of the embedded payload. Depending upon the application it is the designer\u27s task to find an optimum solution amongst them. The goal of this thesis is to develop a novel data hiding scheme to establish a covert channel satisfying statistical and perceptual invisibility with moderate rate capacity and robustness to combat steganalysis based detection. The idea behind the proposed method is the alteration of Video Object (VO) trajectory coordinates to convey the message to the receiver side by perturbing the centroid coordinates of the VO. Firstly, the VO is selected by the user and tracked through the frames by using a simple region based search strategy and morphological operations. After the trajectory coordinates are obtained, the perturbation of the coordinates implemented through the usage of a non-linear embedding function, such as a polar quantizer where both the magnitude and phase of the motion is used. However, the perturbations made to the motion magnitude and phase were kept small to preserve the semantic meaning of the object motion trajectory. The proposed method is well suited to the video sequences in which VOs have smooth motion trajectories. Examples of these types could be found in sports videos in which the ball is the focus of attention and exhibits various motion types, e.g., rolling on the ground, flying in the air, being possessed by a player, etc. Different sports video sequences have been tested by using the proposed method. Through the experimental results, it is shown that the proposed method achieved the goal of both statistical and perceptual invisibility with moderate rate embedding capacity under AWGN channel with varying noise variances. This achievement is important as the first step for both active and passive steganalysis is the detection of the existence of covert channel. This work has multiple contributions in the field of data hiding. Firstly, it is the first example of a data hiding method in which the trajectory of a VO is used. Secondly, this work has contributed towards improving steganographic security by providing new features: the coordinate location and semantic meaning of the object

Destruction of stego images formed by adaptive embedding methods with dictionary learning methods

Counteraction to sensitive information leakage that processed by state and private organizations is topical task today. Of special interest are methods for prevention data leakage by usage of hidden (steganographic) communication channels by attackers. Despite wide range of proposed steganalysis methods for detection of embedded messages, theirs performance highly depends on prior information about used embedding methods. As an example, we may mention modern stegdetectors for digital images, which are based on cover rich models and deep convolutional neural networks. Therefore, the stego image destruction methods are widely applied as preventive action. Modern methods for stego image destruction are based on widespread image denoising methods, like median filter and lossy compression. The limitation of such methods is significant changes of image’s statistical features that may disclosure the steganalysis process to attacker. Therefore, development of stego images processing methods that provide reliable destruction of embedded data, and preserving cover image statistical features is needed. The paper is aimed at performance evaluation of applying the novel methods of spectral analysis, namely dictionary learning, for solving this tasks. The obtained results showed limitation of state-of-the-art methods for destruction of stego image formed by adaptive embedding methods, namely considerable changes of image’s statistical parameters. The proposed method allows preserving both minimal changes of a Cover Image (CI) parameters, and ratio of survived bits of embedded message (less than 7%). This makes proposed solution an attractive candidate for reliable destruction of stego images formed by novel embedding methods. However, practical usage of proposed solution requires further improvement of dictionary learning methods, namely decreasing of computation complexity of dictionary forming procedure

17. Simpozij „Materijali i metalurgija“ – dopuna „Zbornik sažetaka”

In Metalurgija 63 (2024) 2,303-320 published „ Book of Abstracts “ (224). Deadline for received of Abstracts was November, 30,2023 y. Many authors have request new deadline by March, 25, 2024 y. Organizing committee have accept new deadline. Now it published supplements of 103 Abstracts.U Metalurgiji 63 (2024) 2,303-320 objavljen je Zbornik sažetaka (224). Rok za primitak sažetke je bio 30. studeni 2023. god. Mnogi autori zatražili novi rok do 25.03.2024. Organizacijski odbor Simpozija je prihvatio novi termin. Objavljuje se sada dodatnih još 160 sažetaka