525 research outputs found
Are GAN generated images easy to detect? A critical analysis of the state-of-the-art
The advent of deep learning has brought a significant improvement in the
quality of generated media. However, with the increased level of photorealism,
synthetic media are becoming hardly distinguishable from real ones, raising
serious concerns about the spread of fake or manipulated information over the
Internet. In this context, it is important to develop automated tools to
reliably and timely detect synthetic media. In this work, we analyze the
state-of-the-art methods for the detection of synthetic images, highlighting
the key ingredients of the most successful approaches, and comparing their
performance over existing generative architectures. We will devote special
attention to realistic and challenging scenarios, like media uploaded on social
networks or generated by new and unseen architectures, analyzing the impact of
suitable augmentation and training strategies on the detectors' generalization
ability.Comment: 7 pages, 5 figures, conferenc
Towards Robust GAN-generated Image Detection: a Multi-view Completion Representation
GAN-generated image detection now becomes the first line of defense against
the malicious uses of machine-synthesized image manipulations such as
deepfakes. Although some existing detectors work well in detecting clean, known
GAN samples, their success is largely attributable to overfitting unstable
features such as frequency artifacts, which will cause failures when facing
unknown GANs or perturbation attacks. To overcome the issue, we propose a
robust detection framework based on a novel multi-view image completion
representation. The framework first learns various view-to-image tasks to model
the diverse distributions of genuine images. Frequency-irrelevant features can
be represented from the distributional discrepancies characterized by the
completion models, which are stable, generalized, and robust for detecting
unknown fake patterns. Then, a multi-view classification is devised with
elaborated intra- and inter-view learning strategies to enhance view-specific
feature representation and cross-view feature aggregation, respectively. We
evaluated the generalization ability of our framework across six popular GANs
at different resolutions and its robustness against a broad range of
perturbation attacks. The results confirm our method's improved effectiveness,
generalization, and robustness over various baselines.Comment: Accepted to IJCAI 202
Global Texture Enhancement for Fake Face Detection in the Wild
Generative Adversarial Networks (GANs) can generate realistic fake face
images that can easily fool human beings.On the contrary, a common
Convolutional Neural Network(CNN) discriminator can achieve more than 99.9%
accuracyin discerning fake/real images. In this paper, we conduct an empirical
study on fake/real faces, and have two important observations: firstly, the
texture of fake faces is substantially different from real ones; secondly,
global texture statistics are more robust to image editing and transferable to
fake faces from different GANs and datasets. Motivated by the above
observations, we propose a new architecture coined as Gram-Net, which leverages
global image texture representations for robust fake image detection.
Experimental results on several datasets demonstrate that our Gram-Net
outperforms existing approaches. Especially, our Gram-Netis more robust to
image editings, e.g. down-sampling, JPEG compression, blur, and noise. More
importantly, our Gram-Net generalizes significantly better in detecting fake
faces from GAN models not seen in the training phase and can perform decently
in detecting fake natural images
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