A study on the use of perceptual hashing to detect manipulation of embedded messages in images
원문 초록
Typically, metadata of images are stored in a specific data segment of the image file. However, to securely detect changes, data can also be embedded within images. This follows the goal to invisibly and robustly embed as much information as possible to, ideally, even survive compression. This work searches for embedding principles which allow to distinguish between unintended changes by lossy image compression and malicious manipulation of the embedded message based on the change of its perceptual or robust hash. Different embedding and compression algorithms are compared. The study shows that embedding a message via integer wavelet transform and compression with Karhunen-Loeve-transform yields the best results. However, it was not possible to distinguish between manipulation and compression in all cases.
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Perception-Oriented Latent Coding for High-Performance Compressed Domain Semantic Inference
NIC-RobustBench: A Comprehensive Open-Source Toolkit for Neural Image Compression and Robustness Analysis
DiffO: Single-step Diffusion for Image Compression at Ultra-Low Bitrates
ABC: Adaptive BayesNet Structure Learning for Computational Scalable Multi-task Image Compression
Flexible Mixed Precision Quantization for Learned Image Compression
Structured Pruning and Quantization for Learned Image Compression
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