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Image resampling is a common manipulation in image processing. The forensics of resampling plays an important role in image tampering detection, steganography, and steganalysis. In this paper, we proposed an effective and secure detector, which can simultaneously detect resampling and its forged resampling which is attacked by antiforensic schemes. We find that the interpolation operation used in the resampling and forged resampling makes these two kinds of image show different statistical behaviors from the unaltered images, especially in the high frequency domain. To reveal the traces left by the interpolation, we first apply multidirectional high-pass filters on an image and the residual to create multidirectional differences. Then, the difference is fit into an autoregressive (AR) model. Finally, the AR coefficients and normalized histograms of the difference are extracted as the feature. We assemble the feature extracted from each difference image to construct the comprehensive feature and feed it into support vector machines (SVM) to detect resampling and forged resampling. Experiments on a large image database show that the proposed detector is effective and secure. Compared with the state-of-the-art works, the proposed detector achieved significant improvements in the detection of downsampling or resampling under JPEG compression

Revealing Traces of Image Resampling and Resampling Antiforensics