deepfake.md

A3. Deepfake

• [2024/12] Are We in the AI-Generated Text World Already? Quantifying and Monitoring AIGT on Social Media
• [2024/11] Passive Deepfake Detection Across Multi-modalities: A Comprehensive Survey
• [2024/11] AI-generated Image Detection: Passive or Watermark?
• [2024/11] A Quality-Centric Framework for Generic Deepfake Detection
• [2024/10] Humanizing the Machine: Proxy Attacks to Mislead LLM Detectors
• [2024/10] Characterizing the MrDeepFakes Sexual Deepfake Marketplace
• [2024/10] Social Media Authentication and Combating Deepfakes using Semi-fragile Invisible Image Watermarking
• [2024/09] FIDAVL: Fake Image Detection and Attribution using Vision-Language Model
• [2024/09] LLM Detectors Still Fall Short of Real World: Case of LLM-Generated Short News-Like Posts
• [2024/08] MAGE: Machine-generated Text Detection in the Wild
• [2024/08] Are AI-Generated Text Detectors Robust to Adversarial Perturbations?
• [2024/08] Event-Radar: Event-driven Multi-View Learning for Multimodal Fake News Detection
• [2024/08] Unveiling Opinion Evolution via Prompting and Diffusion for Short Video Fake News Detection
• [2024/06] Investigating the Influence of Prompt-Specific Shortcuts in AI Generated Text Detection
• [2024/06] Applying Ensemble Methods to Model-Agnostic Machine-Generated Text Detection
• [2024/06] Evading AI-Generated Content Detectors using Homoglyphs
• [2024/06] Are AI-Generated Text Detectors Robust to Adversarial Perturbations?
• [2024/05] Transformer and Hybrid Deep Learning Based Models for Machine-Generated Text Detection
• [2024/05] RAID: A Shared Benchmark for Robust Evaluation of Machine-Generated Text Detectors
• [2024/05] Who Wrote This? The Key to Zero-Shot LLM-Generated Text Detection Is GECScore
• [2024/04] An Analysis of Recent Advances in Deepfake Image Detection in an Evolving Threat Landscape
• [2024/04] The Adversarial AI-Art: Understanding, Generation, Detection, and Benchmarking
• [2024/04] DeepFake-O-Meter v2.0: An Open Platform for DeepFake Detection
• [2024/04] Watermark-based Detection and Attribution of AI-Generated Content
• [2024/04] Humanizing Machine-Generated Content: Evading AI-Text Detection through Adversarial Attack
• [2024/03] Can ChatGPT Detect DeepFakes? A Study of Using Multimodal Large Language Models for Media Forensics
• [2024/03] The Impact of Prompts on Zero-Shot Detection of AI-Generated Text
• [2024/03] Quantitative Analysis of AI-Generated Texts in Academic Research: A Study of AI Presence in Arxiv Submissions using AI Detection Tool
• [2024/03] Re-Search for The Truth: Multi-round Retrieval-augmented Large Language Models are Strong Fake News Detectors
• [2024/03] GPT-generated Text Detection: Benchmark Dataset and Tensor-based Detection Method
• [2024/03] Towards Detecting AI-Generated Text within Human-AI Collaborative Hybrid Texts
• [2024/03] A Survey of AI-generated Text Forensic Systems: Detection, Attribution, and Characterization
• [2024/02] Technical Report on the Checkfor.ai AI-Generated Text Classifier
• [2024/02] VGMShield: Mitigating Misuse of Video Generative Models
• [2024/02] M4GT-Bench: Evaluation Benchmark for Black-Box Machine-Generated Text Detection
• [2024/02] Ten Words Only Still Help: Improving Black-Box AI-Generated Text Detection via Proxy-Guided Efficient Re-Sampling
• [2024/02] Does DETECTGPT Fully Utilize Perturbation? Selective Perturbation on Model-Based Contrastive Learning Detector would be Better
• [2024/02] TELLER: A Trustworthy Framework for Explainable, Generalizable and Controllable Fake News Detection
• [2024/02] Organic or Diffused: Can We Distinguish Human Art from AI-generated Images?
• [2024/01] Raidar: geneRative AI Detection viA Rewriting
• [2024/01] Spotting LLMs With Binoculars: Zero-Shot Detection of Machine-Generated Text
• [2024/01] Enhancing Robustness of LLM-Synthetic Text Detectors for Academic Writing: A Comprehensive Analysis
• [2024/01] Authorship Obfuscation in Multilingual Machine-Generated Text Detection
• [2024/01] Few-Shot Detection of Machine-Generated Text using Style Representations
• [2024/01] LLM-as-a-Coauthor: The Challenges of Detecting LLM-Human Mixcase
• [2023/10] Harnessing the Power of ChatGPT in Fake News: An In-Depth Exploration in Generation, Detection and Explanation
• [2023/09] Can LLM-Generated Misinformation be Detected?
• [2023/09] Detecting Machine-Generated Texts by Multi-Population Aware Optimization for Maximum Mean Discrepancy
• [2023/09] Robustness of AI-Image Detectors: Fundamental Limits and Practical Attacks
• [2023/06] On the Detectability of ChatGPT Content: Benchmarking, Methodology, and Evaluation through the Lens of Academic Writing
• [2023/05] On the Risk of Misinformation Pollution with Large Language Models
• [2023/05] Evading Watermark based Detection of AI-Generated Content
• [2023/04] Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions
• [2023/03] Can AI-Generated Text be Reliably Detected?
• [2023/03] MGTBench: Benchmarking Machine-Generated Text Detection
• [2022/12] Discovering Language Model Behaviors with Model-Written Evaluations
• [2022/12] CoCo: Coherence-Enhanced Machine-Generated Text Detection Under Data Limitation With Contrastive Learning
• [2022/10] DE-FAKE: Detection and Attribution of Fake Images Generated by Text-to-Image Generation Models