DeepFake Detection
134 papers with code • 5 benchmarks • 18 datasets
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
Benchmarks
These leaderboards are used to track progress in DeepFake Detection
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Libraries
Use these libraries to find DeepFake Detection models and implementationsDatasets
FaceForensics++
Celeb-DF
DFDC
FaceForensics
FakeAVCeleb
WildDeepfake
DeeperForensics-1.0
WaveFake
Latest papers with no code
Training-Free Deepfake Voice Recognition by Leveraging Large-Scale Pre-Trained Models
In this paper we study the potential of large-scale pre-trained models for audio deepfake detection, with special focus on generalization ability.
In Anticipation of Perfect Deepfake: Identity-anchored Artifact-agnostic Detection under Rebalanced Deepfake Detection Protocol
To bridge this gap, we introduce the Rebalanced Deepfake Detection Protocol (RDDP) to stress-test detectors under balanced scenarios where genuine and forged examples bear similar artifacts.
Exploring Self-Supervised Vision Transformers for Deepfake Detection: A Comparative Analysis
This paper investigates the effectiveness of self-supervised pre-trained transformers compared to supervised pre-trained transformers and conventional neural networks (ConvNets) for detecting various types of deepfakes.
Towards Quantitative Evaluation of Explainable AI Methods for Deepfake Detection
The findings of our quantitative and qualitative evaluations document the advanced performance of the LIME explanation method against the other compared ones, and indicate this method as the most appropriate for explaining the decisions of the utilized deepfake detector.
Fake Artificial Intelligence Generated Contents (FAIGC): A Survey of Theories, Detection Methods, and Opportunities
In recent years, generative artificial intelligence models, represented by Large Language Models (LLMs) and Diffusion Models (DMs), have revolutionized content production methods.
Retrieval-Augmented Audio Deepfake Detection
With recent advances in speech synthesis including text-to-speech (TTS) and voice conversion (VC) systems enabling the generation of ultra-realistic audio deepfakes, there is growing concern about their potential misuse.
Texture-aware and Shape-guided Transformer for Sequential DeepFake Detection
In this paper, we propose a novel Texture-aware and Shape-guided Transformer to enhance detection performance.
FreqBlender: Enhancing DeepFake Detection by Blending Frequency Knowledge
Existing methods typically generate these faces by blending real or fake faces in color space.
DeepFake-O-Meter v2.0: An Open Platform for DeepFake Detection
Furthermore, it serves as an evaluation and benchmarking platform for researchers in digital media forensics to compare the performance of multiple algorithms on the same input.
Towards More General Video-based Deepfake Detection through Facial Feature Guided Adaptation for Foundation Model
With the rise of deep learning, generative models have enabled the creation of highly realistic synthetic images, presenting challenges due to their potential misuse.
