Proactive Image Manipulation Detection
Vishal Asnani, Xi Yin, Tal Hassner, Sijia Liu, Xiaoming Liu

TL;DR
This paper introduces a proactive image manipulation detection method that estimates templates to improve detection accuracy across various generative models, outperforming prior passive detection techniques.
Contribution
The paper proposes a novel proactive detection scheme that estimates templates to enhance manipulation detection, demonstrating significant improvements over existing passive methods.
Findings
Outperforms prior work by 16% for CycleGAN and 32% for GauGAN.
Generalizes well to multiple GMs with a 10% average improvement.
Achieves higher accuracy by using template estimation to discriminate manipulated images.
Abstract
Image manipulation detection algorithms are often trained to discriminate between images manipulated with particular Generative Models (GMs) and genuine/real images, yet generalize poorly to images manipulated with GMs unseen in the training. Conventional detection algorithms receive an input image passively. By contrast, we propose a proactive scheme to image manipulation detection. Our key enabling technique is to estimate a set of templates which when added onto the real image would lead to more accurate manipulation detection. That is, a template protected real image, and its manipulated version, is better discriminated compared to the original real image vs. its manipulated one. These templates are estimated using certain constraints based on the desired properties of templates. For image manipulation detection, our proposed approach outperforms the prior work by an average…
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Taxonomy
TopicsImage Processing Techniques and Applications · Digital Media Forensic Detection · Advanced Image Processing Techniques
Methods*Communicated@Fast*How Do I Communicate to Expedia? · Residual Connection · Batch Normalization · HuMan(Expedia)||How do I get a human at Expedia? · PatchGAN · Residual Block · GAN Least Squares Loss · Sigmoid Activation · Tanh Activation · Cycle Consistency Loss
