By Yun-Qing Shi, Hyoung Joong Kim, Fernando Pérez-González, Isao Echizen
This booklet constitutes revised chosen papers from the 14th overseas Workshop on Digital-Forensics and Watermarking, IWDW 2015, held in Tokyo, Japan, in October 2015.
The 35 papers awarded during this quantity have been rigorously reviewed and chosen from fifty four submissions. The contributions are geared up in topical sections named: electronic forensics; steganography and steganalysis; electronic watermarking; reversible information hiding; and visible cryptography.
Read or Download Digital-Forensics and Watermarking: 14th International Workshop, IWDW 2015, Tokyo, Japan, October 7-10, 2015, Revised Selected Papers PDF
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Additional resources for Digital-Forensics and Watermarking: 14th International Workshop, IWDW 2015, Tokyo, Japan, October 7-10, 2015, Revised Selected Papers
Suggested using the colors in images for discrimination . They observed that the colors of natural images are typically more abundant than those of computer-generated ones. g. lacunarity, smoothness, entropy, consistency, and multifractal dimension). However, this method is not appropriate for facial images because such images have colors that are more balanced. Lyu and Farid  proposed using the statistics of the ﬁrst and higher-order wavelets. However, wavelet statistics are better suited for natural images than facial images due to the correlation of facial features.
Region 2 contains a small suspicious part (the building) and has a little great diﬀerence of translation. Since most of Region 2 is true, our method predicts that it is authentic as well. Region 3 is totally authentic and has small diﬀerences of both translation and rotation. In video (b), our method can point out the tampered region as well. Video (c) is the authentic version of video (a). The translational diﬀerence and rotational diﬀerence are both much smaller than the thresholds and no region is predicted fake.
5 Conclusions In this paper, we proposed to utilize the ensemble projection vector as features for camera model identiﬁcation with limited labeled samples and amount unlabeled samples. We carried out experiment to compare LBP algorithm and EP algorithm, and the result demonstrated that our proposed method EP has better performance when the labeled samples is limited. At the same time, the proposed method has robustness to parameter T and r, when the L surpass one value. In a future work, we will focus on improving the classiﬁcation accuracy rate, and consider introduce new feature into our scheme.