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Digital Forensics and Watermarking: 21st International Workshop, IWDW 2022, Guilin, China, November 18-19, 2022, Revised Selected Papers: Lecture Notes in Computer Science, cartea 13825

Editat de Xianfeng Zhao, Zhenjun Tang, Pedro Comesaña-Alfaro, Alessandro Piva
en Limba Engleză Paperback – 29 ian 2023
This book constitutes the refereed proceedings of the 21st International Workshop, IWDW 2022, held in Guilin, China, during November 18-19, 2022. 

The 14 full papers included in this book were carefully reviewed and selected from 30 submissions. They were organized in topical sections as follows: Steganology, Forensics and Security Analysis, Watermarking.
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Specificații

ISBN-13: 9783031251146
ISBN-10: 3031251148
Pagini: 219
Ilustrații: XII, 219 p. 94 illus., 76 illus. in color.
Dimensiuni: 155 x 235 mm
Greutate: 0.33 kg
Ediția:1st ed. 2023
Editura: Springer Nature Switzerland
Colecția Springer
Seria Lecture Notes in Computer Science

Locul publicării:Cham, Switzerland

Cuprins

Steganology.- High-Performance Steganographic Coding Based on Sub-Polarized Channel.- High-Capacity Adaptive Steganography Based on Transform Coefficient for HEVC.- Forensics and Security Analysis.- SE-ResNet56: Robust Network Model for Deepfake Detection.- Voice Conversion Using Learnable Similarity-Guided Masked Autoencoder.- Visual Explanations for Exposing Potential Inconsistency of Deepfakes.- Improving the Transferability of Adversarial Attacks through Both Front and Rear Vector Method.- Manipulated Face Detection and Localization Based on Semantic Segmentation.- Deep Learning Image Age Approximation - What is more Relevant: Image Content or Age Information?.- Watermarking.-  Physical Anti-Copying Semi-Robust Random Watermarking for QR Code.- Robust and Imperceptible Watermarking Scheme for GWAS Data Traceability.- Adaptive Robust Watermarking Method Based on Deep Neural Networks.- Adaptive Despread Spectrum-Based Image Watermarking for Fast Product Tracking.- Reversible Data Hiding via Arranging Blocks of Bit-Planes in Encrypted Images.- High Capacity Reversible Data Hiding for Encrypted 3D Mesh Models Based on Topology.