On the robustness of JPEG post-compression to resampling factor estimation

Zhang, Qin; Lu, Wei; Huang, Tao; Luo, Shangjun; Xu, Zhaopeng; Mao, Yijun

doi:10.1016/j.sigpro.2019.107371

Cited by 10 publications

(1 citation statement)

References 18 publications

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“…More related works can be found in [14][15][16][17]. Among all the resampling detection methods, spectrum-based method [18][19][20][21] is one of the most widely used approach which is capable of resampling factor estimation as well. However, a weakness of spectrum-based methods is ambiguity which greatly influences the accuracy of factor estimation.…”

Section: Introductionmentioning

confidence: 99%

Resampling Factor Estimation via Dual-stream Convolutional Neural Network

Luo¹,

Liu²,

Lu³

et al. 2020

Computers, Materials &Amp; Continua

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The estimation of image resampling factors is an important problem in image forensics. Among all the resampling factor estimation methods, spectrumbased methods are one of the most widely used methods and have attracted a lot of research interest. However, because of inherent ambiguity, spectrum-based methods fail to discriminate upscale and downscale operations without any prior information. In general, the application of resampling leaves detectable traces in both spatial domain and frequency domain of a resampled image. Firstly, the resampling process will introduce correlations between neighboring pixels. In this case, a set of periodic pixels that are correlated to their neighbors can be found in a resampled image. Secondly, the resampled image has distinct and strong peaks on spectrum while the spectrum of original image has no clear peaks. Hence, in this paper, we propose a dual-stream convolutional neural network for image resampling factors estimation. One of the two streams is gray stream whose purpose is to extract resampling traces features directly from the rescaled images. The other is frequency stream that discovers the differences of spectrum between rescaled and original images. The features from two streams are then fused to construct a feature representation including the resampling traces left in spatial and frequency domain, which is later fed into softmax layer for resampling factor estimation. Experimental results show that the proposed method is effective on resampling factor estimation and outperforms some CNN-based methods.

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