Large-Scale JPEG Image Steganalysis Using Hybrid Deep-Learning Framework

Zeng, Jishen; Tan, Shunquan; Li, Bin; Huang, Jiwu

doi:10.1109/tifs.2017.2779446

Cited by 184 publications

(90 citation statements)

References 33 publications

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“…The experiments are run on a NVIDIA Tesla K80 GPU platform. The embedding payload is measured by bits per non-zero cover AC DCT coefficient (bpnzAC) as in [12], [26], [27]. In Section IV-B and IV-C, we conduct experiments on 0.1, 0.2, 0.3, 0.4, and 0.5 bpnzAC.…”

Section: A Settingsmentioning

confidence: 99%

CNN-Based Adversarial Embedding for Image Steganography

Tang

Tan

et al. 2019

IEEE Trans.Inform.Forensic Secur.

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223

132

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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. Index TermsSteganography, steganalysis, adversarial machine learning. I. INTRODUCTIONImage steganography is the art and science of concealing covert information within images. It is usually achieved by modifying image elements, such as pixels or DCT coefficients. On the other side of the game, steganalysis aims to reveal the presence of secret information by detecting whether there are abnormal artefacts left by data embedding.The developing history of steganography and steganalysis is rich of interesting stories, as they compete with each other and they benefit and evolve from the competition [1]. The earliest steganographic method was implemented by substituting the least significant bits of image elements with message bits. The stego artefacts introduced by this method can be effectively detected by Chi-squared attack [2], or steganalytic features based on first-order statistics [3]. In this initial phase of the competition, statistical hypothesis testing or a simple linear classifier such as FLD (Fisher Linear Discriminant) could serve the need of steganalysis.The first-order statistics can be restored after data embedding, as done in [4]. The abnormal artefacts in the first-order statistics can also be avoided as in [5], [6]. As a consequence, more powerful steganalytic features based on the second-order statistics [7], [8] were proposed. In this period, advanced machine learning (ML) tools, such as SVM (Support Vector Machine), were operated on high-dimensional features (where the dimension is typically several hundreds). These met...

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Section: A Settingsmentioning

confidence: 99%

CNN-Based Adversarial Embedding for Image Steganography

Tang

Tan

et al. 2019

IEEE Trans.Inform.Forensic Secur.

Self Cite

223

132

View full text Add to dashboard Cite

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“…YeNet used 30 handcrafted filters from SRM to prepropose images, applied well-designed activation function named TLU and selection-channel module to strengthen features from rich texture region where is more suitable for hiding information. Zeng et al [6] proposed a JPEG steganalysis model with less parameters than XuNet and got better performance than XuNet. These works have applied deep learning to steganalysis successfully, but there is still space for improvement.…”

Section: Related Workmentioning

confidence: 99%

Invisible steganography via generative adversarial networks

Zhang

Dong

Liu

2018

Multimed Tools Appl

161

119

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Nowadays, there are plenty of works introducing convolutional neural networks (CNNs) to the steganalysis and exceeding conventional steganalysis algorithms. These works have shown the improving potential of deep learning in information hiding domain. There are also several works based on deep learning to do image steganography, but these works still have problems in capacity, invisibility and security. In this paper, we propose a novel CNN architecture named as ISGAN to conceal a secret gray image into a color cover image on the sender side and exactly extract the secret image out on the receiver side. There are three contributions in our work: (i) we improve the invisibility by hiding the secret image only in the Y channel of the cover image; (ii) We introduce the generative adversarial networks to strengthen the security by minimizing the divergence between the empirical probability distributions of stego images and natural images. (iii) In order to associate with the human visual system better, we construct a mixed loss function which is more appropriate for steganography to generate more realistic stego images and reveal out more better secret images. Experiment results show that ISGAN can achieve start-of-art performances on LFW, PASCAL-VOC12 and ImageNet datasets.

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“…This purpose, an expert encoder and decomposition scheme was suggested, that offered a high probability of transmission of + 1 bits without causing an increase in pixel variation caused by message concealment. Jishen Zeng [4] developed a generalized hybrid learning model of JPEG format steganalysis integrate the domain knowledge for the construction of an affluent steganalytic models. There were two main stages involved in the proposed framework.…”

Section: Cryptographymentioning

confidence: 99%

LSB based Steganography Techniques for Secured Communication

Kaushal¹,

Kakkar²

2018

IJCA

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Steganography is a data hiding technique which is used to prevent the data or any relevant information from falling to the hands of an unauthorized personal. So, in order to prevent such scenario the steganographic method is used using LSB matching technique in which the LSB of the pixels of the cover media is replaced with that of the relevant information and the cover media is chosen based on characteristics known to only the sender and receiver and no one else can access it. The steganographic technique can find its relevance and scope in merely many field like Marketing, MNC's, Industry. The literature review has been done by keeping in mind to obtain ideas and techniques to detect and improve as much as possible to the steganographic techniques and through research and work done on MATLAB software, were able to detect LSB based steganographic encrypted image from the others. To this some remarks were added to improve the security and evident in result section.

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Large-Scale JPEG Image Steganalysis Using Hybrid Deep-Learning Framework

Cited by 184 publications

References 33 publications

CNN-Based Adversarial Embedding for Image Steganography

CNN-Based Adversarial Embedding for Image Steganography

Invisible steganography via generative adversarial networks

LSB based Steganography Techniques for Secured Communication

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