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Blind Separation of Tampered Image Based on JPEG Double Quantization Effect
Duan Xintao,Peng Tao,Huang Jingjing,Li feifei,Wang Jingjuan 보안공학연구지원센터 2016 International Journal of Signal Processing, Image Vol.9 No.11
The double quantization effect of JPEG provides important clue for detecting image tampering. Whenever an original JPEG image has undergone a localized tampering and saved in JPEG again, the DCT coefficients of the areas without tampering will be compressed for twice while the tampered areas only suffered once. The Alternating Current (AC) coefficient distribution accord with a Laplace probability density distribution described with parameter. This paper proposed a new double compression probability model of JPEG image to describe the change of DCT coefficients’ statistical properties after the double compression. According to Bayes’ theorem, using the posterior probability, the model can also show the eigenvalues of the double and single compressed block. We assign a dynamic adaptive threshold for the eigenvalues with the Particle Swarm Optimization Algorithm. Then the tampered region is detected and separated automatically by using the threshold. The experimental results show that the method can detect and separate the tamped area effectively and it outperforms other algorithms in terms of the detection result especially when the second compression factor is smaller than the first one. Compared with other traditional methods, the proposed approach could effectively separate the tampered regions from the tampered image without respect to the location, size and number of tampered images.
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Image Steganography Based on Pyramid Pooling Module
Bingxin Wei,Xintao Duan,Haewoon Nam 한국통신학회 2022 한국통신학회 학술대회논문집 Vol.2022 No.2
In this paper, we add a pyramid pooling module to the depth model of image steganography, which can effectively improve the visual effect of steganographic images. The goal of the study is to fully integrate previously important global features to achieve good hiding and extraction effects and reduce information loss while ensuring security effects. The experiments are conducted by randomly selecting images from the ImageNet dataset for training and testing the effect on different datasets. The experimental results show that the peak signal-to-noise ratio (PSNR) and structural similarity ratio (SSIM) between images obtained by this method can obtain good values, which brings further visual improvement for image steganography tasks.
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