Papers with Code 아카이브
DeepFake Detection
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 5
- 평가표
- 11
- 보존된 평가 기록
- 30
DeepFake Detection is the task of detecting fake videos or images that have been generated using deep learning techniques. Deepfakes are created by using machine learning algorithms to manipulate or replace parts of an original video or image, such as the face of a person. The goal of deepfake detection is to identify such manipulations and distinguish them from real videos or images.
Description source: DeepFakes: a New Threat to Face Recognition? Assessment and Detection
Image source: DeepFakes: a New Threat to Face Recognition? Assessment and Detection
논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
DDL: A Dataset for Interpretable Deepfake Detection and Localization in Real-World Scenarios
Pay Less Attention to Deceptive Artifacts: Robust Detection of Compressed Deepfakes on Online Social Networks
IndieFake Dataset: A Benchmark Dataset for Audio Deepfake Detection
SELFI: Selective Fusion of Identity for Generalizable Deepfake Detection
Pushing the Performance of Synthetic Speech Detection with Kolmogorov-Arnold Networks and Self-Supervised Learning Models
FAME: A Lightweight Spatio-Temporal Network for Model Attribution of Face-Swap Deepfakes
From Sharpness to Better Generalization for Speech Deepfake Detection
LLMs Are Not Yet Ready for Deepfake Image Detection
Enhancing Deepfake Detection using SE Block Attention with CNN
Unmasking real-world audio deepfakes: A data-centric approach
Context-aware TFL: A Universal Context-aware Contrastive Learning Framework for Temporal Forgery Localization
Lightweight Joint Audio-Visual Deepfake Detection via Single-Stream Multi-Modal Learning Framework
TADA: Training-free Attribution and Out-of-Domain Detection of Audio Deepfakes
DeepFake Doctor: Diagnosing and Treating Audio-Video Fake Detection
Practical Manipulation Model for Robust Deepfake Detection
AuthGuard: Generalizable Deepfake Detection via Language Guidance
A Data-Driven Diffusion-based Approach for Audio Deepfake Explanations
Are Mamba-based Audio Foundation Models the Best Fit for Non-Verbal Emotion Recognition?
Multiverse Through Deepfakes: The MultiFakeVerse Dataset of Person-Centric Visual and Conceptual Manipulations
Source Tracing of Synthetic Speech Systems Through Paralinguistic Pre-Trained Representations
Benchmarking Foundation Models for Zero-Shot Biometric Tasks
Rehearsal with Auxiliary-Informed Sampling for Audio Deepfake Detection
TalkingHeadBench: A Multi-Modal Benchmark & Analysis of Talking-Head DeepFake Detection
Few-Shot Speech Deepfake Detection Adaptation with Gaussian Processes
Guard Me If You Know Me: Protecting Specific Face-Identity from Deepfakes
STOPA: A Database of Systematic VariaTion Of DeePfake Audio for Open-Set Source Tracing and Attribution
ArVoice: A Multi-Speaker Dataset for Arabic Speech Synthesis
EnvSDD: Benchmarking Environmental Sound Deepfake Detection
Preserving AUC Fairness in Learning with Noisy Protected Groups
HyperFake: Hyperspectral Reconstruction and Attention-Guided Analysis for Advanced Deepfake Detection
Think Twice before Adaptation: Improving Adaptability of DeepFake Detection via Online Test-Time Adaptation
CAMME: Adaptive Deepfake Image Detection with Multi-Modal Cross-Attention
Beyond Face Swapping: A Diffusion-Based Digital Human Benchmark for Multimodal Deepfake Detection
CAD: A General Multimodal Framework for Video Deepfake Detection via Cross-Modal Alignment and Distillation
Forensic deepfake audio detection using segmental speech features
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.