How to augment a small learning set for improving the performances of a CNN-based steganalyzer?

Yedroudj, Mehdi; Chaumont, Marc; Comby, Frédéric

doi:10.2352/issn.2470-1173.2018.07.mwsf-317

Cited by 25 publications

(19 citation statements)

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“…At this point, it was not clear if the improvement was only due to a lack of data or also because the additional images came from the same cameras. We have nevertheless conducted additional experiments, reported in the paper [26], and it seems that in order to improve the performance, one must increase the database with images coming from the same sources and with a development process respecting the pixels resolutions and ratios.…”

Section: Results With a Base Augmentationmentioning

confidence: 99%

Yedroudj-Net: An Efficient CNN for Spatial Steganalysis

Yedroudj

Comby

Chaumont

2018

2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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203

152

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For about 10 years, detecting the presence of a secret message hidden in an image was performed with an Ensemble Classifier trained with Rich features. In recent years, studies such as Xu et al. have indicated that well-designed convolutional Neural Networks (CNN) can achieve comparable performance to the two-step machine learning approaches.In this paper, we propose a CNN that outperforms the state-ofthe-art in terms of error probability. The proposition is in the continuity of what has been recently proposed and it is a clever fusion of important bricks used in various papers. Among the essential parts of the CNN, one can cite the use of a pre-processing filterbank and a Truncation activation function, five convolutional layers with a Batch Normalization associated with a Scale Layer, as well as the use of a sufficiently sized fully connected section. An augmented database has also been used to improve the training of the CNN.Our CNN was experimentally evaluated against S-UNIWARD and WOW embedding algorithms and its performances were compared with those of three other methods: an Ensemble Classifier plus a Rich Model, and two other CNN steganalyzers.

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Section: Results With a Base Augmentationmentioning

confidence: 99%

Yedroudj-Net: An Efficient CNN for Spatial Steganalysis

Yedroudj

Comby

Chaumont

2018

2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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203

152

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“…For these networks, the number of images needed to reach the region of good performance (that is the performance of a Rich Model [8] with an Ensemble Classifier [14]), is about 10,000 images (5,000 covers and 5,000 stegos) for the learning phase. This is the case when there is no cover-source mismatch, and the images' size is 256 × 256 pixels [23].…”

Section: Introductionmentioning

confidence: 99%

“…However, this quantity of images is insufficient [23] in the sense that performance can be increased simply by augmenting the size of the training set. In steganalysis, the so-called irreducible error region [10] probably requires many more images than those normally used today.…”

Section: Introductionmentioning

confidence: 99%

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Pixels-off: Data-augmentation Complementary Solution for Deep-learning Steganalysis

Yedroudj

Chaumont

Comby

et al. 2020

Proceedings of the 2020 ACM Workshop on Information Hiding and Multimedia Security

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“…Studies of embedding algorithm involve the former, and existing research on carrier effect, such as cover source mismatch [23][24][25] , carrier selection [26][27][28] , and calibration [29,30] , indicates that the latter has a significant influence on steganalysis. In other words, carrier signal disturbance or carrier selection before embedding helps hide information.…”

Section: Introductionmentioning

confidence: 99%

Fast carrier selection of JPEG steganography appropriate for application

Ren

Xu²,

Zhai

et al. 2020

Tinshhua Sci. Technol.

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In recent years, the improvement of the security of steganography mainly involves not only carrier security but also distortion function. In the actual environment, the existing method of carrier selection is limited by its complex algorithm and slow running speed, making it not appropriate for rapid communication. This study proposes a method for selecting carriers and improving the security of steganography. JPEG images are decompressed to spatial domain. Then correlation coefficients between two adjacent pixels in the horizontal, vertical, counter diagonal, and major diagonal directions are calculated. The mean value of the four correlation coefficients is used to evaluate the security of each JPEG image. The images with low correlation coefficients are considered safe carriers and used for embedding in our scheme. The experimental results indicate that the stego images generated from the selected carriers exhibit a higher anti-steganalysis capability than those generated from the randomly selected carriers. Under the premise of the same security level, the images with low correlation coefficients have a high capacity. Our algorithm has a very fast running speed, and the running time of a 2048 2048 image is less than 1 s.

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How to augment a small learning set for improving the performances of a CNN-based steganalyzer?

Cited by 25 publications

References 0 publications

Yedroudj-Net: An Efficient CNN for Spatial Steganalysis

Yedroudj-Net: An Efficient CNN for Spatial Steganalysis

Pixels-off: Data-augmentation Complementary Solution for Deep-learning Steganalysis

Fast carrier selection of JPEG steganography appropriate for application

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