SOTAVerified

Synthetic Image Detection

Identify if the image is real or generated/manipulated by any generative models (GAN or Diffusion).

Papers

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Showing 11–20 of 28 papers

TitleStatusHype
ImagiNet: A Multi-Content Benchmark for Synthetic Image DetectionCode1
TextureCrop: Enhancing Synthetic Image Detection through Texture-based CroppingCode0
TGIF: Text-Guided Inpainting Forgery DatasetCode1
When Synthetic Traces Hide Real Content: Analysis of Stable Diffusion Image LaunderingCode0
SIDBench: A Python Framework for Reliably Assessing Synthetic Image Detection MethodsCode2
E3: Ensemble of Expert Embedders for Adapting Synthetic Image Detectors to New Generators Using Limited DataCode0
Harnessing the Power of Large Vision Language Models for Synthetic Image DetectionCode1
Bi-LORA: A Vision-Language Approach for Synthetic Image DetectionCode1
Leveraging Representations from Intermediate Encoder-blocks for Synthetic Image DetectionCode1
Harnessing Machine Learning for Discerning AI-Generated Synthetic Images—0
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