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Deepfakes & multimodalDetection methods
The Emergence of Deepfake Technology: A ReviewThis review analyzes 84 news articles about deepfakes to synthesize understanding of synthetic video technology, its applications, threats, and countermeasures. Deepfakes—hyper-realistic videos created using AI (primarily GANs)—lower the barrier to video forgery and represent a significant threat to trust, elections, and security.
Deepfakes & multimodalDetection methods
Deep Learning for Deepfakes Creation and Detection: A SurveyComprehensive survey of deep learning methods for creating and detecting deepfakes, covering face-swapping and lip-syncing generation techniques (GANs, autoencoders, encoder-decoder pairs), detection approaches (visual artifacts, temporal features, forensics), and challenges in cross-dataset generalization. The paper discusses…
Bots & coordinated campaignsDeepfakes & multimodal
Characterizing the Use of Images in State-Sponsored Information Warfare Operations by Russian Trolls on TwitterLarge-scale analysis of 1.8M images posted by ~3.6K Russian state-sponsored Twitter accounts (IRA-controlled). Russian trolls strategically deployed images and memes to maximize political influence, with political imagery 2+ times more effective than general content.
Deepfakes & multimodalDetection methods
Recurrent Convolutional Strategies for Face Manipulation Detection in VideosThis paper proposes using recurrent convolutional networks to detect face manipulations (Deepfake, Face2Face, FaceSwap) in videos by exploiting temporal discrepancies across frames. Face preprocessing with alignment and bidirectional recurrent networks achieve state-of-the-art on FaceForensics++ with up to 4.55% improvement over prior…
Deepfakes & multimodalDetection methods
FaceForensics++: Learning to Detect Manipulated Facial ImagesFaceForensics++ introduces a large-scale benchmark (1.8+ million manipulated images from 1,000+ videos) for detecting facial manipulations created with four state-of-the-art methods: Face2Face, FaceSwap, DeepFakes, and NeuralTextures. The paper comprehensively evaluates detection methods from stegananalysis to deep learning,…
Datasets & evaluationDeepfakes & multimodal
r/Fakeddit: A New Multimodal Benchmark Dataset for Fine-grained Fake News DetectionFakeddit is a large-scale multimodal fake news detection dataset with over 1 million Reddit samples labeled with 2-way, 3-way, and 6-way classification schemes. Multimodal models combining text (BERT) and image (ResNet50) features achieve 86.0% accuracy on 6-way classification, demonstrating the importance of jointly leveraging text and…
Deepfakes & multimodalDetection methods
FakeCatcher: Detection of Synthetic Portrait Videos using Biological SignalsFakeCatcher detects deepfakes by analyzing biological signals (photoplethysmography—blood flow patterns) extracted from facial regions. The key insight is that generative models fail to preserve spatial coherence and temporal consistency of these signals in authentic videos, making biological signal analysis a powerful forensic tool…
LLMs & generative AIDetection methods
Defending Against Neural Fake NewsZellers et al. introduce GROVER, a conditional neural text generation model that can both generate and detect neural fake news. The paper shows that humans cannot reliably distinguish GROVER-generated propaganda from real human-written disinformation, rating machine-generated propaganda as more trustworthy (2.42/3 vs 2.19/3 for human…
Deepfakes & multimodalDetection methods
MVAE: Multimodal Variational Autoencoder for Fake News DetectionThe paper proposes MVAE, a variational autoencoder that learns shared representations of text and images in social media posts to detect fake news. The model jointly trains an encoder-decoder (to discover correlations across modalities) with a binary classifier.