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Titlebook: Digital Forensics and Watermarking; 22nd International W Bin Ma,Jian Li,Qi Li Conference proceedings 2024 The Editor(s) (if applicable) and

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发表于 2025-3-21 18:11:32 | 显示全部楼层 |阅读模式
书目名称Digital Forensics and Watermarking
副标题22nd International W
编辑Bin Ma,Jian Li,Qi Li
视频video
丛书名称Lecture Notes in Computer Science
图书封面Titlebook: Digital Forensics and Watermarking; 22nd International W Bin Ma,Jian Li,Qi Li Conference proceedings 2024 The Editor(s) (if applicable) and
描述.This book constitutes the refereed post proceedings of the 22nd International Workshop on Digital Forensics and Watermarking, IWDW 2023, held in Jinan, China,.during November 25–26, 2023...The 22 full papers included in this book were carefully reviewed and selected from 48 submissions. The workshop focuses on subjects such as novel research, development and application of digital watermarking, data hiding, and forensic techniques for multimedia security..
出版日期Conference proceedings 2024
关键词artificial intelligence; computer networks; cryptography; data security; digital watermarking; Forensics;
版次1
doihttps://doi.org/10.1007/978-981-97-2585-4
isbn_softcover978-981-97-2584-7
isbn_ebook978-981-97-2585-4Series ISSN 0302-9743 Series E-ISSN 1611-3349
issn_series 0302-9743
copyrightThe Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapor
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High-Quality PRNU Anonymous Algorithm for JPEG Images is employed, enabling the recovery of traceability if necessary. Our algorithm offers several advantages over existing schemes. It operates within the domain of JPEG image compression, maintaining a low time complexity. Additionally, it effectively preserves the visual quality of images and elimina
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From Deconstruction to Reconstruction: A Plug-In Module for Diffusion-Based Purification of Adversarexamples while preserving high classification accuracy on clean image samples. The model performance is evaluated on representative neural network classifiers pre-trained and fine-tuned on large-scale datasets. An ablation study analyses the impact of the proposed plug-in module on enhancing the eff
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Cross-channel Image Steganography Based on Generative Adversarial Networkearning rates and weight decay values. At the same time, the mean square error loss, structural similarity loss, and adversarial loss are employed to progressively enhance the visual quality of generated stego images. Consequently, a color image can be seamlessly embedded into the same-sized color i
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