A 7,000-sample background-manipulation benchmark with matched controls shows that re-encoding artifacts cause false-positive rates of 0.57–1.00 across all tested baselines.
Learning JPEG Compression Artifacts for Image Manipulation Detection and Localization
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abstract
Detecting and localizing image manipulation are necessary to counter malicious use of image editing techniques. Accordingly, it is essential to distinguish between authentic and tampered regions by analyzing intrinsic statistics in an image. We focus on JPEG compression artifacts left during image acquisition and editing. We propose a convolutional neural network (CNN) that uses discrete cosine transform (DCT) coefficients, where compression artifacts remain, to localize image manipulation. Standard CNNs cannot learn the distribution of DCT coefficients because the convolution throws away the spatial coordinates, which are essential for DCT coefficients. We illustrate how to design and train a neural network that can learn the distribution of DCT coefficients. Furthermore, we introduce Compression Artifact Tracing Network (CAT-Net) that jointly uses image acquisition artifacts and compression artifacts. It significantly outperforms traditional and deep neural network-based methods in detecting and localizing tampered regions.
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cs.CV 1years
2026 1verdicts
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BG-REAL: A Public Real-Data Anchored Benchmark for Background Manipulation Detection and Localization
A 7,000-sample background-manipulation benchmark with matched controls shows that re-encoding artifacts cause false-positive rates of 0.57–1.00 across all tested baselines.