DeepFake Detection
129 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
WildDeepfake
FakeAVCeleb
DeeperForensics-1.0
KoDF
Latest papers
Heterogeneity over Homogeneity: Investigating Multilingual Speech Pre-Trained Models for Detecting Audio Deepfake
To validate our hypothesis, we extract representations from state-of-the-art (SOTA) PTMs including monolingual, multilingual as well as PTMs trained for speaker and emotion recognition, and evaluated them on ASVSpoof 2019 (ASV), In-the-Wild (ITW), and DECRO benchmark databases.
AVT2-DWF: Improving Deepfake Detection with Audio-Visual Fusion and Dynamic Weighting Strategies
With the continuous improvements of deepfake methods, forgery messages have transitioned from single-modality to multi-modal fusion, posing new challenges for existing forgery detection algorithms.
Can ChatGPT Detect DeepFakes? A Study of Using Multimodal Large Language Models for Media Forensics
DeepFakes, which refer to AI-generated media content, have become an increasing concern due to their use as a means for disinformation.
Learning Spatiotemporal Inconsistency via Thumbnail Layout for Face Deepfake Detection
The deepfake threats to society and cybersecurity have provoked significant public apprehension, driving intensified efforts within the realm of deepfake video detection.
Frequency-Aware Deepfake Detection: Improving Generalizability through Frequency Space Learning
chuangchuangtan/freqnet-deepfakedetection
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Consequently, these detectors have exhibited a lack of proficiency in learning the frequency domain and tend to overfit to the artifacts present in the training data, leading to suboptimal performance on unseen sources.
Data-Independent Operator: A Training-Free Artifact Representation Extractor for Generalizable Deepfake Detection
chuangchuangtan/data-independent-operator
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Due to its unbias towards both the training and test sources, we define it as Data-Independent Operator (DIO) to achieve appealing improvements on unseen sources.
Exposing the Deception: Uncovering More Forgery Clues for Deepfake Detection
qingyuliu/exposing-the-deception
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In this paper, we try to tackle these challenges through three designs: (1) We present a novel framework to capture broader forgery clues by extracting multiple non-overlapping local representations and fusing them into a global semantic-rich feature.
Preserving Fairness Generalization in Deepfake Detection
purdue-m2/fairness-generalization
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The existing method for addressing this problem is providing a fair loss function.
CLIPping the Deception: Adapting Vision-Language Models for Universal Deepfake Detection
sohailahmedkhan/CLIPping-the-Deception
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The recent advancements in Generative Adversarial Networks (GANs) and the emergence of Diffusion models have significantly streamlined the production of highly realistic and widely accessible synthetic content.
Deepfake Detection and the Impact of Limited Computing Capabilities
PalomaCantero/Deepfake-Detection-and-the-Impact-of-Limited-Computing-Capabilities
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The rapid development of technologies and artificial intelligence makes deepfakes an increasingly sophisticated and challenging-to-identify technique.
