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Titlebook: Digital Forensics and Watermarking; 16th International W Christian Kraetzer,Yun-Qing Shi,Hyoung Joong Kim Conference proceedings 2017 Sprin

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Block-Based Convolutional Neural Network for Image Forgery Detectiond, these visually convincing tampering operations also make authentication of digital images difficult. Therefore, developing a precise and robust method to detect these splicing images is urgently required. In the past, some researchers proposed some methods, which achieves an accuracy of over 97%,
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Detection of Face Morphing Attacks by Deep Learning and are nowadays standard components in many mobile devices. Thus, many devices can be unlocked via fingerprint or face verification. The state of the art accuracy of biometric facial recognition systems prompted even systems that need high security standards like border control at airports to rely
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Topological Data Analysis for Image Tampering Detectionalysis (TDA) concept of persistent homological invariants associated with certain image features. Image features of interest are pixels that have a uniform Local Binary pattern (LBP) code representing texture feature descriptors. We construct the sequence of simplicial complexes for increasing seque
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Coding Efficiency Preserving Steganography Based on HEVC Steganographic Channel Modelfor some important factors in steganography, such as capacity, distortion, is hard to obtain. In this paper, to avoid introducing significant distortion into HEVC video file, a novel HEVC SCM is presented and analyzed. It is firstly proposed that the distortion optimization method in this SCM should
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