# Generalizable Synthetic Image Detection via Language-guided Contrastive Learning

arXiv: https://arxiv.org/abs/2305.13800 · HTML: https://mlsanity.com/paper/2305.13800/

## Key facts

- Cited by 49 papers in the map (data through 14 Oct 2026).
- Placed in Image generation, text-to-image and editing, one of 14 directions in Diffusion models.
- Builds on 67 of its 101 references that are in the map.
- First posted on arXiv 23 May 2023.

## Placed in

- Diffusion models / [Image generation, text-to-image and editing](https://mlsanity.com/diffusion/image-generation-text-to-image-and-editing/)

## Builds on (most cited first, 30 of 67)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [Diffusion Models Beat GANs on Image Synthesis](https://mlsanity.com/paper/2105.05233/) (2021)
- [Momentum Contrast for Unsupervised Visual Representation Learning](https://mlsanity.com/paper/1911.05722/) (2019)
- [Masked Autoencoders Are Scalable Vision Learners](https://mlsanity.com/paper/2111.06377/) (2021)
- [Hierarchical Text-Conditional Image Generation with CLIP Latents](https://mlsanity.com/paper/2204.06125/) (2022)
- [A Style-Based Generator Architecture for Generative Adversarial Networks](https://mlsanity.com/paper/1812.04948/) (2018)
- [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter](https://mlsanity.com/paper/1910.01108/) (2019)
- [GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models](https://mlsanity.com/paper/2112.10741/) (2021)
- [Analyzing and Improving the Image Quality of StyleGAN](https://mlsanity.com/paper/1912.04958/) (2019)
- [A ConvNet for the 2020s](https://mlsanity.com/paper/2201.03545/) (2022)
- [Taming Transformers for High-Resolution Image Synthesis](https://mlsanity.com/paper/2012.09841/) (2020)
- [Towards Evaluating the Robustness of Neural Networks](https://mlsanity.com/paper/1608.04644/) (2016)
- [Progressive Growing of GANs for Improved Quality, Stability, and Variation](https://mlsanity.com/paper/1710.10196/) (2017)
- [SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers](https://mlsanity.com/paper/2105.15203/) (2021)
- [ALBERT: A Lite BERT for Self-supervised Learning of Language Representations](https://mlsanity.com/paper/1909.11942/) (2019)
- [Large Scale GAN Training for High Fidelity Natural Image Synthesis](https://mlsanity.com/paper/1809.11096/) (2018)
- [LSUN: Construction of a Large-scale Image Dataset using Deep Learning with Humans in the Loop](https://mlsanity.com/paper/1506.03365/) (2015)
- [Semantic Image Synthesis with Spatially-Adaptive Normalization](https://mlsanity.com/paper/1903.07291/) (2019)
- [Oscar: Object-Semantics Aligned Pre-training for Vision-Language Tasks](https://mlsanity.com/paper/2004.06165/) (2020)
- [Efficient Geometry-aware 3D Generative Adversarial Networks](https://mlsanity.com/paper/2112.07945/) (2021)
- [Alias-Free Generative Adversarial Networks](https://mlsanity.com/paper/2106.12423/) (2021)
- [StarGAN: Unified Generative Adversarial Networks for Multi-Domain Image-to-Image Translation](https://mlsanity.com/paper/1711.09020/) (2017)
- [Open-vocabulary Object Detection via Vision and Language Knowledge Distillation](https://mlsanity.com/paper/2104.13921/) (2021)
- [CNN-generated images are surprisingly easy to spot... for now](https://mlsanity.com/paper/1912.11035/) (2019)
- [Domain Generalization: A Survey](https://mlsanity.com/paper/2103.02503/) (2021)
- [Score-based Generative Modeling in Latent Space](https://mlsanity.com/paper/2106.05931/) (2021)

## Built on by (most cited first, 30 of 49)

- [C2P-CLIP: Injecting Category Common Prompt in CLIP to Enhance Generalization in Deepfake Detection](https://mlsanity.com/paper/2408.09647/) (2024)
- [Orthogonal Subspace Decomposition for Generalizable AI-Generated Image Detection](https://mlsanity.com/paper/2411.15633/) (2024)
- [DeMamba: AI-Generated Video Detection on Million-Scale GenVideo Benchmark](https://mlsanity.com/paper/2405.19707/) (2024)
- [Detecting Multimedia Generated by Large AI Models: A Survey](https://mlsanity.com/paper/2402.00045/) (2024)
- [LaRE2: Latent Reconstruction Error Based Method for Diffusion-Generated Image Detection](https://mlsanity.com/paper/2403.17465/) (2024)
- [Detecting Images Generated by Deep Diffusion Models using their Local Intrinsic Dimensionality](https://mlsanity.com/paper/2307.02347/) (2023)
- [AIGI-Holmes: Towards Explainable and Generalizable AI-Generated Image Detection via Multimodal Large Language Models](https://mlsanity.com/paper/2507.02664/) (2025)
- [WildFake: A Large-scale Challenging Dataset for AI-Generated Images Detection](https://mlsanity.com/paper/2402.11843/) (2024)
- [Bridging the Gap Between Ideal and Real-world Evaluation: Benchmarking AI-Generated Image Detection in Challenging Scenarios](https://mlsanity.com/paper/2509.09172/) (2025)
- [Bi-LORA: A Vision-Language Approach for Synthetic Image Detection](https://mlsanity.com/paper/2404.01959/) (2024)
- [Learning Human-Perceived Fakeness in AI-Generated Videos via Multimodal LLMs](https://mlsanity.com/paper/2509.22646/) (2025)
- [Language-guided Hierarchical Fine-grained Image Forgery Detection and Localization](https://mlsanity.com/paper/2410.23556/) (2024)
- [Methods and Trends in Detecting AI-Generated Images: A Comprehensive Review](https://mlsanity.com/paper/2502.15176/) (2025)
- [Dual Frequency Branch Framework with Reconstructed Sliding Windows Attention for AI-Generated Image Detection](https://mlsanity.com/paper/2501.15253/) (2025)
- [RAIDX: A Retrieval-Augmented Generation and GRPO Reinforcement Learning Framework for Explainable Deepfake Detection](https://mlsanity.com/paper/2508.04524/) (2025)
- [Detecting Origin Attribution for Text-to-Image Diffusion Models](https://mlsanity.com/paper/2403.19653/) (2024)
- [Premonition: Using Generative Models to Preempt Future Data Changes in Continual Learning](https://mlsanity.com/paper/2403.07356/) (2024)
- [Artifact Feature Purification for Cross-domain Detection of AI-generated Images](https://mlsanity.com/paper/2403.11172/) (2024)
- [Multimodal Conditional Information Bottleneck for Generalizable AI-Generated Image Detection](https://mlsanity.com/paper/2505.15217/) (2025)
- [DA-HFNet: Progressive Fine-Grained Forgery Image Detection and Localization Based on Dual Attention](https://mlsanity.com/paper/2406.01489/) (2024)
- [Semantics-Oriented Multitask Learning for DeepFake Detection: A Joint Embedding Approach](https://mlsanity.com/paper/2408.16305/) (2024)
- [Unveiling the Truth: Exploring Human Gaze Patterns in Fake Images](https://mlsanity.com/paper/2403.08933/) (2024)
- [Present and Future Generalization of Synthetic Image Detectors](https://mlsanity.com/paper/2409.14128/) (2024)
- [FontGuard: A Robust Font Watermarking Approach Leveraging Deep Font Knowledge](https://mlsanity.com/paper/2504.03128/) (2025)
- [FIDAVL: Fake Image Detection and Attribution using Vision-Language Model](https://mlsanity.com/paper/2409.03109/) (2024)
- [Time Step Generating: A Universal Synthesized Deepfake Image Detector](https://mlsanity.com/paper/2411.11016/) (2024)
- [Semantic-Aware Reconstruction Error for Detecting AI-Generated Images](https://mlsanity.com/paper/2508.09487/) (2025)
- ["Humor, Art, or Misinformation?": A Multimodal Dataset for Intent-Aware Synthetic Image Detection](https://mlsanity.com/paper/2508.20670/) (2025)
- [Generalized Design Choices for Deepfake Detectors](https://mlsanity.com/paper/2511.21507/) (2025)
- [FIND: A Simple yet Effective Baseline for Diffusion-Generated Image Detection](https://mlsanity.com/paper/2603.14220/) (2026)

## Abstract (arXiv)

The heightened realism of AI-generated images can be attributed to the rapid development of synthetic models, including generative adversarial networks (GANs) and diffusion models (DMs). The malevolent use of synthetic images, such as the dissemination of fake news or the creation of fake profiles, however, raises significant concerns regarding the authenticity of images. Though many forensic algorithms have been developed for detecting synthetic images, their performance, especially the generalization capability, is still far from being adequate to cope with the increasing number of synthetic models. In this work, we propose a simple yet very effective synthetic image detection method via a language-guided contrastive learning. Specifically, we augment the training images with carefully-designed textual labels, enabling us to use a joint visual-language contrastive supervision for learning a forensic feature space with better generalization. It is shown that our proposed LanguAge-guided SynThEsis Detection (LASTED) model achieves much improved generalizability to unseen image generation models and delivers promising performance that far exceeds state-of-the-art competitors over four datasets. The code is available at https://github.com/HighwayWu/LASTED.