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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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發(fā)表于 2025-3-21 18:11:32 | 只看該作者 |倒序瀏覽 |閱讀模式
書目名稱Digital Forensics and Watermarking
副標題22nd International W
編輯Bin Ma,Jian Li,Qi Li
視頻videohttp://file.papertrans.cn/280/279332/279332.mp4
叢書名稱Lecture Notes in Computer Science
圖書封面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
關鍵詞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
The information of publication is updating

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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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