Double JPEG compression forensics based on a convolutional neural network

EURASIP Journal on Information Security, Oct 2016

Double JPEG compression detection has received considerable attention in blind image forensics. However, only few techniques can provide automatic localization. To address this challenge, this paper proposes a double JPEG compression detection algorithm based on a convolutional neural network (CNN). The CNN is designed to classify histograms of discrete cosine transform (DCT) coefficients, which differ between single-compressed areas (tampered areas) and double-compressed areas (untampered areas). The localization result is obtained according to the classification results. Experimental results show that the proposed algorithm performs well in double JPEG compression detection and forgery localization, especially when the first compression quality factor is higher than the second.

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Double JPEG compression forensics based on a convolutional neural network

Wang and Zhang EURASIP Journal on Information Security Double JPEG compression forensics based on a convolutional neural network Qing Wang 0 1 Rong Zhang 0 1 0 Key Laboratory of Electromagnetic Space Information, Chinese Academy of Sciences , Huangshan Road, 230027 Hefei , China 1 Department of Electronic Engineering and Information Science, University of Science and Technology of China , Huangshan Road, Hefei , China Double JPEG compression detection has received considerable attention in blind image forensics. However, only few techniques can provide automatic localization. To address this challenge, this paper proposes a double JPEG compression detection algorithm based on a convolutional neural network (CNN). The CNN is designed to classify histograms of discrete cosine transform (DCT) coefficients, which differ between single-compressed areas (tampered areas) and double-compressed areas (untampered areas). The localization result is obtained according to the classification results. Experimental results show that the proposed algorithm performs well in double JPEG compression detection and forgery localization, especially when the first compression quality factor is higher than the second. Blind image forensics; Double JPEG compression; Convolutional neural network; Classification 1 Introduction Generally, blind forensics techniques utilize statistical and geometrical features, interpolation effects, or feature inconsistencies to verify the authenticity of image/videos when no prior knowledge of the original sources is available. Because JPEG compression may cover certain traces of digital tampering, many techniques are effective only on uncompressed images. However, most multimedia capture devices and post-processing software suites such as Photoshop, output images in the JPEG format, and most digital images on the internet are also JPEG images. Hence, developing blind forensics techniques that are robust to JPEG compression is vital. Tampering with JPEG images often involves recompression, i.e., resaving the forged image in the JPEG format with a different compression quality factor after digital tampering, which may introduce evidence of double JPEG compression. Recently, many successful double JPEG compression detection algorithms have been proposed. Lukáš and Fridrich [1] and Popescu and Farid [2] have performed some pioneering work. They analyzed the double quantization (DQ) effect before and after tampering and found that the discrete cosine transform (DCT) coefficient’s histograms for an image region that has been quantized twice generally show a periodicity, differing from the DCT coefficient’s histograms for a single-quantized region. Chen and Hsu [3] identified periodic compression artifacts in DCT coefficients in either the spatial or Fourier domain, which can detect both block-aligned and nonaligned double JPEG compression. Fu et al. [4] and Li et al. [5] reported that DCT coefficients of single-compressed images generally follow Benford’s law, whereas those of double-compressed images violate it. In [5], they detect double-compressed JPEG images by using mode-based first digit features combined with Fisher linear discriminant (FLD) analysis. Fridrich et al. [6] applied double JPEG compression in steganography. The feature they used is derived from the statistics of low-frequency DCT coefficients, and it is effective not only for normal forged images but also for images processed using steganographic algorithms. However, a commonality among all algorithms discussed above is that they estimate only the compression history of an image, which cannot indicate exactly which region has been manipulated. In fact, the localization of tampered regions is a basic necessity for meaningful image forgery detection. Nevertheless, to the best of our knowledge, only few forensics algorithms can achieve it. Lin et al.’s algorithm [7] was the first to automatically locate local tampered areas by analyzing the DQ effects hidden among the DCT coefficient’s histograms. The authors applied the Bayesian approach to estimate the © 2016 The Author(s). Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. probabilities of individual 8 × 8 block being untampered. In this way, the obtained block posterior probability map (BPPM) would show a visual difference between tampered (single-compressed) regions and unchanged (double-compressed) regions. To locate the tampered regions more accurately, Wang et al. [8] utilized the prior knowledge that a tampered region should be smooth and clustered and minimized a defined energy function using the graph cut algorithm to lo (...truncated)


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Qing Wang, Rong Zhang. Double JPEG compression forensics based on a convolutional neural network, EURASIP Journal on Information Security, 2016, pp. 23, Volume 2016, Issue 1, DOI: 10.1186/s13635-016-0047-y