Synthetic Image Detection
18 papers with code • 0 benchmarks • 2 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
SuSy Dataset
Gap Pattern Detection
Most implemented papers
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.
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.
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.
Forgery-aware Adaptive Transformer for Generalizable Synthetic Image Detection
Michel-liu/FatFormer
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CVPR 2024
In this paper, we study the problem of generalizable synthetic image detection, aiming to detect forgery images from diverse generative methods, e. g., GANs and diffusion models.
Leveraging Representations from Intermediate Encoder-blocks for Synthetic Image Detection
mever-team/rine
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The recently developed and publicly available synthetic image generation methods and services make it possible to create extremely realistic imagery on demand, raising great risks for the integrity and safety of online information.
Bi-LORA: A Vision-Language Approach for Synthetic Image Detection
Advancements in deep image synthesis techniques, such as generative adversarial networks (GANs) and diffusion models (DMs), have ushered in an era of generating highly realistic images.
Harnessing the Power of Large Vision Language Models for Synthetic Image Detection
This study contributes to the advancement of synthetic image detection by exploiting the capabilities of visual language models such as BLIP-2 and ViTGPT2.
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.
