SOTAVerified

DeepFake Detection

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

Papers

Showing 110 of 580 papers

TitleStatusHype
SHIELD: A Secure and Highly Enhanced Integrated Learning for Robust Deepfake Detection against Adversarial Attacks0
DDL: A Dataset for Interpretable Deepfake Detection and Localization in Real-World Scenarios0
Pay Less Attention to Deceptive Artifacts: Robust Detection of Compressed Deepfakes on Online Social NetworksCode0
IndieFake Dataset: A Benchmark Dataset for Audio Deepfake Detection0
SELFI: Selective Fusion of Identity for Generalizable Deepfake Detection0
FAME: A Lightweight Spatio-Temporal Network for Model Attribution of Face-Swap DeepfakesCode0
From Sharpness to Better Generalization for Speech Deepfake Detection0
LLMs Are Not Yet Ready for Deepfake Image Detection0
Enhancing Deepfake Detection using SE Block Attention with CNN0
Unmasking real-world audio deepfakes: A data-centric approachCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1FasterThanLiesAUC1Unverified