ForgeryNet
A Comprehensive Benchmark for Deepfakes and Forgery Detection

An open-source digital image forensic toolset for experimenting with image analysis algorithms.
Sherloq is an open-source digital image forensics toolset designed for experimenting with various algorithms found in research papers and workshops. It provides an integrated environment for digital image forensics, aiming to be an extensible framework and a starting point for digital signal processing applications. The tool is built with a Qt-based GUI in Python (PySide2 + Matplotlib + OpenCV), emphasizing ease of development and deployment. Sherloq enables users to perform tasks such as file digest analysis, metadata extraction, image enhancement, noise analysis, and forgery detection using algorithms for copy-move forgery, composite splicing, and resampling detection. It also incorporates machine learning models for tasks like multiple compression detection.
Sherloq is an open-source digital image forensics toolset designed for experimenting with various algorithms found in research papers and workshops.
Explore all tools that specialize in image authentication. This domain focus ensures Sherloq delivers optimized results for this specific requirement.
Explore all tools that specialize in forgery detection. This domain focus ensures Sherloq delivers optimized results for this specific requirement.
Explore all tools that specialize in metadata analysis. This domain focus ensures Sherloq delivers optimized results for this specific requirement.
Explore all tools that specialize in noise analysis. This domain focus ensures Sherloq delivers optimized results for this specific requirement.
Explore all tools that specialize in file analysis. This domain focus ensures Sherloq delivers optimized results for this specific requirement.
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A Comprehensive Benchmark for Deepfakes and Forgery Detection

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