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- W4283220862 abstract "• This is the first work that introduces adversarial mechanism into robust steganography to improve the security of steganography. Adversarial dither modulation algorithm is proposed to guide the dither directions via the signs and magnitudes of cover gradients calculated from pre-trained steganalyzer. • According to “Matching Robust” or “Upward Robust” scene, we select proper embedding elements to balance the robustness against JPEG compression and steganography security. • Extensive experiments show that the proposed adversarial robust steganography could significantly improve the steganography security and maintain strong robustness. Traditional image steganography assumes that the transmission channel is lossless, thus it can not assure the correct extraction of hidden secret messages in real social networks. Recently, some robust image steganographic methods have been proposed to resist lossy JPEG compression. In this paper, we first introduce adversarial mechanism into robust image steganography to enhance steganography security. Instead of modifying embedding costs in traditional adversarial embedding steganography, we propose adversarial dither modulation algorithm according to the cover gradients calculated from a pre-trained CNN (Convolutional Neural Networks)-based steganalyzer. This algorithm determines the dither directions via the signs and magnitudes of the cover gradients. In addition, we carefully select proper embedding elements according to the prior information of the transmission channel to obtain a good tradeoff between “robustness” and “security” for robust steganography. Experimental results show that the proposed method can achieve significant improvements on security while preserving very good robustness compared with related work." @default.
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- W4283220862 date "2022-11-01" @default.
- W4283220862 modified "2023-10-17" @default.
- W4283220862 title "Adversarial robust image steganography against lossy JPEG compression" @default.
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- W4283220862 doi "https://doi.org/10.1016/j.sigpro.2022.108668" @default.
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