Adopt an optimal location using a genetic algorithm for audio steganography

With the development of technologies, most of the users utilizing the Internet for transmitting information from one place to another place. The transmitted data may be affected because of the intermediate user. Therefore, the steganography approach is applied for managing the secret information. Here audio steganography is utilized to maintain the secret information by hiding the image into the audio files. In this work, discrete cosine transforms, and discrete wavelet transform is applied to perform the Steganalysis process. The optimal hiding location has been identified by using the optimization technique called a genetic algorithm. The method utilizes the selection, crossover and mutation operators for selecting the best location. The chosen locations are difficult to predict by unauthorized users because the embedded location is varied from information to information. Then the efficiency of the system ensures the high PSNR, structural similarity index (SSIM), minimum mean square error value and Jaccard, which is evaluated on the audio Steganalysis dataset

Advances in Syndrome Coding based on Stochastic and Deterministic Matrices for Steganography

Steganographie ist die Kunst der vertraulichen Kommunikation. Anders als in der Kryptographie, wo der Austausch vertraulicher Daten für Dritte offensichtlich ist, werden die vertraulichen Daten in einem steganographischen System in andere, unauffällige Coverdaten (z.B. Bilder) eingebettet und so an den Empfänger übertragen. Ziel eines steganographischen Algorithmus ist es, die Coverdaten nur geringfügig zu ändern, um deren statistische Merkmale zu erhalten, und möglichst in unauffälligen Teilen des Covers einzubetten. Um dieses Ziel zu erreichen, werden verschiedene Ansätze der so genannten minimum-embedding-impact Steganographie basierend auf Syndromkodierung vorgestellt. Es wird dabei zwischen Ansätzen basierend auf stochastischen und auf deterministischen Matrizen unterschieden. Anschließend werden die Algorithmen bewertet, um Vorteile der Anwendung von Syndromkodierung herauszustellen

Information Forensics and Security: A quarter-century-long journey

Information forensics and security (IFS) is an active R&D area whose goal is to ensure that people use devices, data, and intellectual properties for authorized purposes and to facilitate the gathering of solid evidence to hold perpetrators accountable. For over a quarter century, since the 1990s, the IFS research area has grown tremendously to address the societal needs of the digital information era. The IEEE Signal Processing Society (SPS) has emerged as an important hub and leader in this area, and this article celebrates some landmark technical contributions. In particular, we highlight the major technological advances by the research community in some selected focus areas in the field during the past 25 years and present future trends

A One-dimensional HEVC video steganalysis method using the Optimality of Predicted Motion Vectors

Among steganalysis techniques, detection against motion vector (MV) domain-based video steganography in High Efficiency Video Coding (HEVC) standard remains a hot and challenging issue. For the purpose of improving the detection performance, this paper proposes a steganalysis feature based on the optimality of predicted MVs with a dimension of one. Firstly, we point out that the motion vector prediction (MVP) of the prediction unit (PU) encoded using the Advanced Motion Vector Prediction (AMVP) technique satisfies the local optimality in the cover video. Secondly, we analyze that in HEVC video, message embedding either using MVP index or motion vector differences (MVD) may destroy the above optimality of MVP. And then, we define the optimal rate of MVP in HEVC video as a steganalysis feature. Finally, we conduct steganalysis detection experiments on two general datasets for three popular steganography methods and compare the performance with four state-of-the-art steganalysis methods. The experimental results show that the proposed optimal rate of MVP for all cover videos is 100\%, while the optimal rate of MVP for all stego videos is less than 100\%. Therefore, the proposed steganography scheme can accurately distinguish between cover videos and stego videos, and it is efficiently applied to practical scenarios with no model training and low computational complexity.Comment: Submitted to TCSV

From Covert Hiding to Visual Editing: Robust Generative Video Steganography

Traditional video steganography methods are based on modifying the covert space for embedding, whereas we propose an innovative approach that embeds secret message within semantic feature for steganography during the video editing process. Although existing traditional video steganography methods display a certain level of security and embedding capacity, they lack adequate robustness against common distortions in online social networks (OSNs). In this paper, we introduce an end-to-end robust generative video steganography network (RoGVS), which achieves visual editing by modifying semantic feature of videos to embed secret message. We employ face-swapping scenario to showcase the visual editing effects. We first design a secret message embedding module to adaptively hide secret message into the semantic feature of videos. Extensive experiments display that the proposed RoGVS method applied to facial video datasets demonstrate its superiority over existing video and image steganography techniques in terms of both robustness and capacity.Comment: Under Revie

Re-encoding Resistance: Towards Robust Covert Channels over WebRTC Video Streaming

Internet censorship is an ongoing phenomenon, where state level agents attempt to control the free access to information on the internet for purposes like dissent suppression and control. In response, research has been dedicated to propose and implement censorship circumvention solutions. One approach to circumvention involves the use of steganography, the process of embedding a hidden message into a cover medium (e.g., image, video, or audio file), such that sensitive or restricted information can be exchanged without a censoring agent being able to detect this exchange. Stegozoa, one such steganography tool, proposes using WebRTC video conferencing as the channel for embedding, to allow a party within a restricted area to freely receive information from a party located outside of this area, circumventing censorship. This project on itself, is an extension of an earlier implementation, and it assumes a stronger threat model, where WebRTC connections are not peer-to-peer but instead mediated by a gateway server, which may be controlled, or influenced, by the censoring agent. In this threat model, it is argued that an attacker (or censor) may inspect the data being transmitted directly, but has no incentive to change the video data. With our work, we seek to challenge this last assumption, since many applications using this WebRTC architecture can and will in fact modify the video, likely for non malicious purposes. By implementing our own test WebRTC application, we have shown that performing video re-encoding (that is decoding a VP8 format video into raw format and then back) on the transmitted data, is enough to render an implementation like Stegozoa inoperable. We argue that re-encoding is commonly a non-malicious operation, which may be justified by the application setup (for example to perform video filtering, or integrity checks, or other types of computer vision operations), and that does not affect a regular non-Stegozoa user. It is for this reason, that we proposed that re-encoding robustness is a necessary feature for steganographic systems. To this end, first we performed characterization experiments on a popular WebRTC video codec (VP8), to understand the effects of re-encoding. Similarly, we tested the effects of this operation when a hidden message is embed in a similar fashion to Stegozoa. We were able to show that, DCT coefficients, which are used commonly as the target for message embedding, change enough to cause loss of message integrity due to re-encoding, without the use of any error correction. Our experiments showed that higher frequency Discrete Cosine Transform (DCT) coefficients are more likely to remain stable for message embedding after re-encoding. We also showed that a dynamically calculated embedding space (that is the set of coefficients that may actually be used for embedding), akin to Stegozoa’s implementation, is very likely to be different after re-encoding, which creates a mismatch between sender and receiver. With these observations, we then sought to test a more robust implementation for embedding. To do so, we combined the usage of error correction (in the form of Reed-Solomon codes), and a static embedding space. We showed that message re-transmission (that is, embedding in multiple frames) and error correction are enough to send a message that will be received correctly. Our experiments showed that this can be used as a low-bandwidth non time-sensitive channel for covert communications. Finally, we combined our results to provide a set of guidelines that we believe are needed to implement a WebRTC based, VP8 encoded, censorship circumvention