Papers with Code 아카이브
Synthetic Image Detection
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Identify if the image is real or generated/manipulated by any generative models (GAN or Diffusion).
논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
CO-SPY: Combining Semantic and Pixel Features to Detect Synthetic Images by AI
LEGION: Learning to Ground and Explain for Synthetic Image Detection
Explainable Synthetic Image Detection through Diffusion Timestep Ensembling
Methods and Trends in Detecting Generated Images: A Comprehensive Review
Orthogonal Subspace Decomposition for Generalizable AI-Generated Image Detection
Present and Future Generalization of Synthetic Image Detectors
Detect Fake with Fake: Leveraging Synthetic Data-driven Representation for Synthetic Image Detection
Improving Synthetic Image Detection Towards Generalization: An Image Transformation Perspective
ImagiNet: A Multi-Content Benchmark for Synthetic Image Detection
TextureCrop: Enhancing Synthetic Image Detection through Texture-based Cropping
TGIF: Text-Guided Inpainting Forgery Dataset
When Synthetic Traces Hide Real Content: Analysis of Stable Diffusion Image Laundering
SIDBench: A Python Framework for Reliably Assessing Synthetic Image Detection Methods
E3: Ensemble of Expert Embedders for Adapting Synthetic Image Detectors to New Generators Using Limited Data
Harnessing the Power of Large Vision Language Models for Synthetic Image Detection
Bi-LORA: A Vision-Language Approach for Synthetic Image Detection
Leveraging Representations from Intermediate Encoder-blocks for Synthetic Image Detection
Harnessing Machine Learning for Discerning AI-Generated Synthetic Images
Forgery-aware Adaptive Transformer for Generalizable Synthetic Image Detection
Synthetic Image Detection: Highlights from the IEEE Video and Image Processing Cup 2022 Student Competition
Generalizable Synthetic Image Detection via Language-guided Contrastive Learning
Deep Image Fingerprint: Towards Low Budget Synthetic Image Detection and Model Lineage Analysis
ArtiFact: A Large-Scale Dataset with Artificial and Factual Images for Generalizable and Robust Synthetic Image Detection
Aggregating Layers for Deepfake Detection
The Value of AI Guidance in Human Examination of Synthetically-Generated Faces
A Closer Look at Fourier Spectrum Discrepancies for CNN-generated Images Detection
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.