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Face Swapping

Face swapping refers to the task of swapping faces between images or in an video, while maintaining the rest of the body and environment context.

( Image credit: Swapped Face Detection using Deep Learning and Subjective Assessment )

Papers

Showing 451500 of 914 papers

TitleStatusHype
ASVspoof 2021: accelerating progress in spoofed and deepfake speech detection0
ASVspoof 5: Crowdsourced Speech Data, Deepfakes, and Adversarial Attacks at Scale0
ASVspoof 5: Design, Collection and Validation of Resources for Spoofing, Deepfake, and Adversarial Attack Detection Using Crowdsourced Speech0
A Systematical Solution for Face De-identification0
A Timely Survey on Vision Transformer for Deepfake Detection0
Attacker Attribution of Audio Deepfakes0
Non-Consensual Synthetic Intimate Imagery: Prevalence, Attitudes, and Knowledge in 10 Countries0
Attribute-Guided Face Generation Using Conditional CycleGAN0
Audio Deepfake Detection Based on a Combination of F0 Information and Real Plus Imaginary Spectrogram Features0
Audio Deepfake Perceptions in College Going Populations0
Audios Don't Lie: Multi-Frequency Channel Attention Mechanism for Audio Deepfake Detection0
Audio-Visual Deepfake Detection With Local Temporal Inconsistencies0
Augmentation through Laundering Attacks for Audio Spoof Detection0
AUNet: Learning Relations Between Action Units for Face Forgery Detection0
A Universal Anti-Spoofing Approach for Contactless Fingerprint Biometric Systems0
AuthGuard: Generalizable Deepfake Detection via Language Guidance0
Automated Deepfake Detection0
AVFF: Audio-Visual Feature Fusion for Video Deepfake Detection0
AV-Lip-Sync+: Leveraging AV-HuBERT to Exploit Multimodal Inconsistency for Video Deepfake Detection0
AVTENet: Audio-Visual Transformer-based Ensemble Network Exploiting Multiple Experts for Video Deepfake Detection0
A Watermark for Auto-Regressive Image Generation Models0
Balancing Creativity and Automation: The Influence of AI on Modern Film Production and Dissemination0
Benchmarking Audio Deepfake Detection Robustness in Real-world Communication Scenarios0
Benchmarking Deepart Detection0
Benchmarking Foundation Models for Zero-Shot Biometric Tasks0
Benchmarking Joint Face Spoofing and Forgery Detection with Visual and Physiological Cues0
BENet: A Cross-domain Robust Network for Detecting Face Forgeries via Bias Expansion and Latent-space Attention0
BERT Transformer model for Detecting Arabic GPT2 Auto-Generated Tweets0
Beyond Deepfake Images: Detecting AI-Generated Videos0
Beyond Detection: Visual Realism Assessment of Deepfakes0
Beyond Face Swapping: A Diffusion-Based Digital Human Benchmark for Multimodal Deepfake Detection0
Big Brother is Watching: Proactive Deepfake Detection via Learnable Hidden Face0
BiHPF: Bilateral High-Pass Filters for Robust Deepfake Detection0
BMRL: Bi-Modal Guided Multi-Perspective Representation Learning for Zero-Shot Deepfake Attribution0
Building an Invisible Shield for Your Portrait against Deepfakes0
CAD: A General Multimodal Framework for Video Deepfake Detection via Cross-Modal Alignment and Distillation0
Can deepfakes be created by novice users?0
Can DeepFake Speech be Reliably Detected?0
Can Multi-modal (reasoning) LLMs work as deepfake detectors?0
Can We Leave Deepfake Data Behind in Training Deepfake Detector?0
Capture Artifacts via Progressive Disentangling and Purifying Blended Identities for Deepfake Detection0
CausalVE: Face Video Privacy Encryption via Causal Video Prediction0
Classifying Deepfakes Using Swin Transformers0
Codecfake: An Initial Dataset for Detecting LLM-based Deepfake Audio0
CodecFake: Enhancing Anti-Spoofing Models Against Deepfake Audios from Codec-Based Speech Synthesis Systems0
Collaborative Evaluation of Deepfake Text with Deliberation-Enhancing Dialogue Systems0
Combatting deepfakes: Policies to address national security threats and rights violations0
Combining Automatic Speaker Verification and Prosody Analysis for Synthetic Speech Detection0
Comparative Analysis of Deep Convolutional Neural Networks for Detecting Medical Image Deepfakes0
Comparative Analysis of Deep-Fake Algorithms0
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