https://github.com/davide-coccomini/Combining-EfficientNet-and-Vision-Transformers-for-Video-Deepfake-Detection
공식 코드Combining EfficientNet and Vision Transformers for Video Deepfake Detection
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
Facial tasks in machine learning operate based on images or video frames (or other datasets) focussed on human faces.
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
Combining EfficientNet and Vision Transformers for Video Deepfake Detection
Combining EfficientNet and Vision Transformers for Video Deepfake Detection
Cross-Forgery Analysis of Vision Transformers and CNNs for Deepfake Image Detection
Adversarial Magnification to Deceive Deepfake Detection through Super Resolution
Deepfake Detection without Deepfakes: Generalization via Synthetic Frequency Patterns Injection
MINTIME: Multi-Identity Size-Invariant Video Deepfake Detection
Multi-Granularity Representation Learning for Sketch-based Dynamic Face Image Retrieval
EQFace: A Simple Explicit Quality Network for Face Recognition
DFIL: Deepfake Incremental Learning by Exploiting Domain-invariant Forgery Clues
ArcFace: Additive Angular Margin Loss for Deep Face Recognition
Wild Face Anti-Spoofing Challenge 2023: Benchmark and Results
Partial FC: Training 10 Million Identities on a Single Machine
Improving Collaborative Metric Learning with Efficient Negative Sampling
Analyzing the Synthetic-to-Real Domain Gap in 3D Hand Pose Estimation
Linguistic Profiling of Deepfakes: An Open Database for Next-Generation Deepfake Detection
Perceptual Quality-preserving Black-Box Attack against Deep Learning Image Classifiers
Joint Co-Speech Gesture and Expressive Talking Face Generation using Diffusion with Adapters
Rethinking Affect Analysis: A Protocol for Ensuring Fairness and Consistency
PTW: Pivotal Tuning Watermarking for Pre-Trained Image Generators
Limited Data, Unlimited Potential: A Study on ViTs Augmented by Masked Autoencoders
Facial Motion Prior Networks for Facial Expression Recognition
Rethinking Bias Mitigation: Fairer Architectures Make for Fairer Face Recognition
Improving the Transferability of Targeted Adversarial Examples through Object-Based Diverse Input
Pose-disentangled Contrastive Learning for Self-supervised Facial Representation
Text-Free Learning of a Natural Language Interface for Pretrained Face Generators
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.