TL;DR
This study investigates the impact of image reconstruction processes using two approaches: Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs) on authentic and forged images, and establishes a foundational understanding of forensic artifacts resulting from the reconstruction process.
A comprehensive review of numerous copy-move forgery detection approaches, analyzing their strengths and limitations, and evaluating their performance across multiple benchmark datasets to inspire the development of more robust CMFD solutions.
Overall, GenPix provides a challenging and realistic benchmark for evaluating modern detectors, and the proposed AAE offers an efficient, interpretable baseline for future research on general-purpose fake-image detection.
This paper provides a systematic review of mainstream deepfake generation models and methods, including Autoencoders, Variational Autoencoders, Variational Autoencoders (VAE), Convolutional Neural Networks (CNN), and Convolutional Neural Networks (CNN), and Generative Adversarial Networks (GAN) etc.
A generative detector that localizes tampering by estimating the local restoration cost required to align a query image with authentic visual-text statistics, rather than by learning forgery-specific decision boundaries is proposed, and Sparse-Constraint Rectified Flow is introduced, a detector-oriented adaptation of F...
The rapid advancement of diffusion models has raised concerns about their misuse in generating deceptive visual content, motivating growing interest in AI-generated image detection. Many existing detection methods rely on image semantic features, but modern diffusion models are optimized to closely match the semantic s...
This work proposes two novel attack methods targeted at detectors that leverage autoencoder reconstruction error and finds that by constructing imperceptible adversarial examples, the distance between original and reconstruction can be artificially increased, causing fake images to be wrongly classified as real.
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