A framework of adaptive steganography resisting JPEG compression and detection.

Current typical adaptive steganography algorithms take the detection resistant capability into account adequately but usually cannot extract the embedded secret messages correctly when stego images suffer from compression attack. In order to solve this problem, a framework of adaptive steganography resisting JPEG compression and detection is proposed. Utilizing the relationship between Discrete Cosine Transformation DCT coefficients, the domain of messages embedding is determined; for the maximum of the JPEG compression resistant ability, the modifying magnitude of different DCT coefficients caused by messages embedding can be determined; in order to ensure the completely correct extraction of embedded messages after JPEG compression, error correct codes are used to encode the messages to be embedded; on the basis of the current distortion functions, the distortion value of DCT coefficients corresponding to the modifying magnitude in the embedding domain can be calculated; to improve the detection resistant ability of the stego images and realize the minimum distortion embedding, syndrome-trellis codes are used to embed the encoded messages into the DCT coefficients that have a smaller distortion value. Based on the proposed framework, an adaptive steganography algorithm resisting JPEG compression and detection is designed, which utilizes the relationship between coefficients in a DCT block and the means of that in three adjacent DCT blocks. The experimental results that demonstrate the proposed algorithm not only has a good JPEG compression resistant ability but also has a strong detection resistant performance. Comparing with current J-UNIWARD steganography under quality factor 85 of JPEG compression, the extraction error rates without pre-compression decrease from about 50% to nearly 0, while the stego images remain a good detection resistant ability comparing with a typical robust watermarking algorithm, which shows the validity of the proposed framework. Copyright © 2016 John Wiley & Sons, Ltd.