Deep Learning Hierarchical Representations for Image Steganalysis

Ye, Jian; Ni, Jiangqun; Yi, Yang

doi:10.1109/tifs.2017.2710946

Cited by 554 publications

(359 citation statements)

References 20 publications

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“…Deep neural networks such as the convolutional neural network (CNN) have the capacity to automatically obtain high-dimensional features and reduce its dimensionality efficiently [8]. Some researchers have begun to utilize deep learning to solve problems in the domain of image forensics, such as image manipulation detection [9], camera model identification [10,11], steganalysis [12,13], image copy-move forgery detection [14], and so on.…”

Section: Introductionmentioning

confidence: 99%

Distinguishing Computer-Generated Graphics from Natural Images Based on Sensor Pattern Noise and Deep Learning

Yao

Zhang

et al. 2018

Sensors

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Computer-generated graphics (CGs) are images generated by computer software. The rapid development of computer graphics technologies has made it easier to generate photorealistic computer graphics, and these graphics are quite difficult to distinguish from natural images (NIs) with the naked eye. In this paper, we propose a method based on sensor pattern noise (SPN) and deep learning to distinguish CGs from NIs. Before being fed into our convolutional neural network (CNN)-based model, these images—CGs and NIs—are clipped into image patches. Furthermore, three high-pass filters (HPFs) are used to remove low-frequency signals, which represent the image content. These filters are also used to reveal the residual signal as well as SPN introduced by the digital camera device. Different from the traditional methods of distinguishing CGs from NIs, the proposed method utilizes a five-layer CNN to classify the input image patches. Based on the classification results of the image patches, we deploy a majority vote scheme to obtain the classification results for the full-size images. The experiments have demonstrated that (1) the proposed method with three HPFs can achieve better results than that with only one HPF or no HPF and that (2) the proposed method with three HPFs achieves 100% accuracy, although the NIs undergo a JPEG compression with a quality factor of 75.

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Section: Introductionmentioning

confidence: 99%

Distinguishing Computer-Generated Graphics from Natural Images Based on Sensor Pattern Noise and Deep Learning

Yao

Zhang

et al. 2018

Sensors

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“…Undetectability. We measure undetectability by training the steganalyzer ATS [LHM16] and YeNet [YNY17] to distinguish between the cover and stego images for all methods. ATS is the steganalysis method, which uses labeled data to build unique artificial training sets and trains an unsupervised classification of stego and cover images.…”

Section: Methodsmentioning

confidence: 99%

HidingGAN: High Capacity Information Hiding with Generative Adversarial Network

Wang

Gao

Wang

et al. 2019

Computer Graphics Forum

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Image steganography is the technique of hiding secret information within images. It is an important research direction in the security field. Benefitting from the rapid development of deep neural networks, many steganographic algorithms based on deep learning have been proposed. However, two problems remain to be solved in which the most existing methods are limited by small image size and information capacity. In this paper, to address these problems, we propose a high capacity image steganographic model named HidingGAN. The proposed model utilizes a new secret information preprocessing method and Inception‐ResNet block to promote better integration of secret information and image features. Meanwhile, we introduce generative adversarial networks and perceptual loss to maintain the same statistical characteristics of cover images and stego images in the high‐dimensional feature space, thereby improving the undetectability. Through these manners, our model reaches higher imperceptibility, security, and capacity. Experiment results show that our HidingGAN achieves the capacity of 4 bits‐per‐pixel (bpp) at 256 × 256 pixels, improving over the previous best result of 0.4 bpp at 32 × 32 pixels.

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“…XuNet was equipped with Tanh, 1 × 1 convolution, global average pool-ing, and got comparable performance to SRM [2]. Ye et al [5] put forward YeNet which surpassed SRM and its several variants. 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.…”

Section: Related Workmentioning

confidence: 99%

“…Yang et al [10] improved ASDL-GAN and achieved better performance than S-UNIWARD. They used Selection-Channel-Aware (SCA) [5] in generator as well as the U-Net framework [28] which is introduced from the medical images segmentation. However, ASDL-GAN still refers too many prior knowledge from conventional steganography algorithms and its capacity is small.…”

Section: Related Workmentioning

confidence: 99%

“…In previous works, Baluja [12] used the mean square error (MSE) between the pixels of original images and the pixels of reconstructed images as the metric (5). Where c and s are the cover and secret images respectively, c and s are the stego and revealed secret images respectively, and β is how to weight their reconstruction errors.…”

Section: Mixed Loss Functionmentioning

confidence: 99%

See 1 more Smart Citation

Invisible steganography via generative adversarial networks

Zhang

Dong

Liu

2018

Multimed Tools Appl

162

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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Deep Learning Hierarchical Representations for Image Steganalysis

Cited by 554 publications

References 20 publications

Distinguishing Computer-Generated Graphics from Natural Images Based on Sensor Pattern Noise and Deep Learning

Distinguishing Computer-Generated Graphics from Natural Images Based on Sensor Pattern Noise and Deep Learning

HidingGAN: High Capacity Information Hiding with Generative Adversarial Network

Invisible steganography via generative adversarial networks

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