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Deepfakes & multimodalDetection methods
A Deep Learning Approach for Multimodal Deception DetectionThis paper proposes a multimodal neural model for detecting deception in real-life courtroom trial videos by combining visual features from 3D-CNN, audio features from openSMILE, textual features from CNN on transcripts, and manual micro-expression annotations. The model achieves 96.14% accuracy and ROC-AUC of 0.9799, substantially…
Platforms, policy & societyDeepfakes & multimodal
The Malicious Use of Artificial Intelligence: Forecasting, Prevention, and MitigationA landmark 2018 report surveying the landscape of potential security threats from malicious uses of AI across three domains—digital, physical, and political security. The work proposes frameworks for forecasting AI-enabled attacks, identifies specific threat scenarios including AI-generated disinformation and deepfakes, and recommends…
Deepfakes & multimodalDetection methods
Two-Stream Neural Networks for Tampered Face DetectionThe paper proposes a two-stream neural network for detecting face tampering (face swaps) in images. One stream uses GoogLeNet to classify tampered faces by visual artifacts, while the second stream uses steganalysis features with a triplet network to capture low-level noise inconsistencies.
Detection methodsDeepfakes & multimodal
TI-CNN: Convolutional Neural Networks for Fake News DetectionThis paper proposes TI-CNN, a multimodal deep learning model that combines explicit and latent features from both text and images to detect fake news. Through analysis of 20,015 news items, the authors identify distinctive linguistic and visual patterns in fake versus real news, and demonstrate that their unified CNN-based approach…
Deepfakes & multimodalDetection methods
Exposing Deep Fakes Using Inconsistent Head PosesDeepfakes splice a synthesized face into an original image, introducing landmark misalignment errors invisible to the eye but detectable through 3D head pose estimation. The method compares head poses estimated from all facial landmarks versus only the central region; real faces show consistent poses while deepfakes exhibit large…
Deepfakes & multimodalDetection methods
Detecting GAN-generated Imagery using Color CuesThis paper identifies two forensic cues to distinguish GAN-generated images from real camera imagery by analyzing how generator networks process color. The first cue exploits overlapping color channel weights in the generator (unlike camera spectral sensitivities); the second leverages the fact that generator normalization suppresses…
Deepfakes & multimodalDetection methods
In Ictu Oculi: Exposing AI Generated Fake Face Videos by Detecting Eye BlinkingDetects DeepFake videos by exploiting the absence of natural eye blinking patterns in AI-generated faces. Uses a Long-term Recurrent Convolutional Network (LRCN) combining CNN for feature extraction with LSTM-RNN for temporal modeling of eye state, achieving 0.99 AUC on authentic vs. synthesized videos—a forensic signal invisible to the…
Deepfakes & multimodalDetection methods
DeepFakes: a New Threat to Face Recognition? Assessment and DetectionGAN-based face-swapping (Deepfakes) pose a significant threat to face recognition systems: VGG and FaceNet achieve false acceptance rates of 85.62% and 95.00% on high-quality Deepfakes. The authors release the first public Deepfake video database (620 videos from 16 subject pairs) and show that image quality metrics with SVM achieve…
Deepfakes & multimodalDatasets & evaluation
MesoNet: A Compact Facial Video Forgery Detection NetworkThis paper proposes two lightweight CNN architectures (Meso-4 and MesoInception-4) to detect facial video forgeries created by Deepfake and Face2Face techniques. Operating at the mesoscopic level of image analysis, the networks achieve 98% detection accuracy for Deepfake and 95% for Face2Face videos under realistic compression conditions.
Deepfakes & multimodalDetection methods
EANN: Event Adversarial Neural Networks for Multi-Modal Fake News DetectionThe paper proposes EANN, an adversarial neural network that detects fake news on newly emerged events by learning event-invariant features. An event discriminator forces the multi-modal feature extractor to remove event-specific features while preserving shared patterns across events.