Synthetic Image Detection
6 papers with code • 0 benchmarks • 0 datasets
Identify if the image is real or generated/manipulated by any generative models (GAN or Diffusion).
Benchmarks
These leaderboards are used to track progress in Synthetic Image Detection
Most implemented papers
A Closer Look at Fourier Spectrum Discrepancies for CNN-generated Images Detection
sutd-visual-computing-group/Fourier-Discrepancies-CNN-Detection
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CVPR 2021
Our results prompt re-thinking of using high frequency Fourier spectrum decay attributes for CNN-generated image detection.
Aggregating Layers for Deepfake Detection
Crucially, most work in this domain assume that the Deepfakes in the test set come from the same Deepfake algorithms that were used for training the network.
ArtiFact: A Large-Scale Dataset with Artificial and Factual Images for Generalizable and Robust Synthetic Image Detection
awsaf49/artifact
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Synthetic image generation has opened up new opportunities but has also created threats in regard to privacy, authenticity, and security.
Deep Image Fingerprint: Towards Low Budget Synthetic Image Detection and Model Lineage Analysis
sergo2020/dif_pytorch_official
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Our method can detect images from a known generative model and enable us to establish relationships between fine-tuned generative models.
Generalizable Synthetic Image Detection via Language-guided Contrastive Learning
highwaywu/lasted
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In this work, we propose a simple yet very effective synthetic image detection method via a language-guided contrastive learning and a new formulation of the detection problem.
E3: Ensemble of Expert Embedders for Adapting Synthetic Image Detectors to New Generators Using Limited Data
arefaz/e3-ensemble-of-expert-embedders-cvprwmf24
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To address these issues, we introduce the Ensemble of Expert Embedders (E3), a novel continual learning framework for updating synthetic image detectors.
