Steganography algorithms recognition based on match image and deep features verification

Steganography algorithms recognition is a sub-section of steganalysis. Analysis shows when a steganalysis detector trained on one cover source is applied to images from an unseen source, generally the detection performance decreases. To tackle with this problem, this paper proposes a steganalytic scheme for steganography algorithms recognition. For a given testing image, a match image of the testing image is achieved. The match image is generated by performing a Gaussian filtering on the testing image to remove the possible stego signal. Then the match image is embedded in with recognized steganography algorithms. A CNN model trained on a training set is used to extract deep features from testing image and match images. Computing similarity between features with inner product operation or weighted-χ2, the final decision is made according to similarity between testing feature and each class of match feature. The proposed scheme can also detect steganography algorithms unknown in training set. Experiments show that, comparing with directly used CNN model, the proposed scheme achieves considerable improvement on testing accuracy when detecting images come from unseen source.

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