GAN image forensics
6 papers with code • 0 benchmarks • 0 datasets
Benchmarks
These leaderboards are used to track progress in GAN image forensics
Latest papers
Can We Find Neurons that Cause Unrealistic Images in Deep Generative Networks?
hichoe95/Artifact-Detection-and-Sequential-Ablation
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Even though Generative Adversarial Networks (GANs) have shown a remarkable ability to generate high-quality images, GANs do not always guarantee the generation of photorealistic images.
CDE-GAN: Cooperative Dual Evolution Based Generative Adversarial Network
In essence, CDE-GAN incorporates dual evolution with respect to the generator(s) and discriminators into a unified evolutionary adversarial framework to conduct effective adversarial multi-objective optimization.
Video Face Manipulation Detection Through Ensemble of CNNs
In this paper, we tackle the problem of face manipulation detection in video sequences targeting modern facial manipulation techniques.
On the use of Benford's law to detect GAN-generated images
polimi-ispl/icpr-benford-gan
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The advent of Generative Adversarial Network (GAN) architectures has given anyone the ability of generating incredibly realistic synthetic imagery.
Detecting and Simulating Artifacts in GAN Fake Images
By using the simulated images to train a spectrum based classifier, even without seeing the fake images produced by the targeted GAN model during training, our approach achieves state-of-the-art performances on detecting fake images generated by popular GAN models such as CycleGAN.
Learning to Detect Fake Face Images in the Wild
jesse1029/Fake-Face-Images-Detection-Tensorflow
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Although Generative Adversarial Network (GAN) can be used to generate the realistic image, improper use of these technologies brings hidden concerns.
