Titlebook: Digital Forensics and Watermarking; 17th International W Chang D. Yoo,Yun-Qing Shi,Gwangsu Kim Conference proceedings 2019 Springer Nature

Benign

Nandita Chaudhary,Shashi Shuklaiction algorithm is essential and crucial. In this paper, a high-performance error-prediction method based on Multiple Linear Regression (MLR) algorithm is proposed to improve the performance of Reversible Data Hiding (RDH). The MLR matrix function that indicates the inner correlations between the p

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https://doi.org/10.1007/978-3-030-11389-6cryptography; digital forensics; watermarking; steganalysis; steganography; security service; data hiding;

恩惠

978-3-030-11388-9Springer Nature Switzerland AG 2019

含水層

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伴隨而來

Rethinking Resistance and Colonialism,ly PEE and MHM to embed the LSB of . to reserve space for secret data. Next, we encrypt the image and change the LSB of . to realize the embedding of secret data. In the process of extraction, the reversibility of image and secret data can be guaranteed. The utilization of correlation between neighb

Consensus

Nandita Chaudhary,Shashi Shuklarovide a sparser prediction-error image for data embedding, and thus improves the performance of RDH. Experimental results have shown that the proposed method outperform the state-of-the-art error prediction algorithms.

invulnerable

Convolutional Neural Network for Larger JPEG Images Steganalysis. 512, 1024 . 1024 and 2048 . 2048. For different application scenes, we take two methods to generate large samples. The result demonstrates that the proposed scheme can make directly training the steganalysis detectors on large images feasible.