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 with no code
Selective Domain-Invariant Feature for Generalizable Deepfake Detection
To solve this problem, we proposed a novel framework Selective Domain-Invariant Feature (SDIF), which reduces the sensitivity to face forgery by fusing content features and styles.
Exploiting Style Latent Flows for Generalizing Deepfake Detection Video Detection
This paper presents a new approach for the detection of fake videos, based on the analysis of style latent vectors and their abnormal behavior in temporal changes in the generated videos.
XAI-Based Detection of Adversarial Attacks on Deepfake Detectors
Furthermore, this approach does not change the performance of the deepfake detector.
Adversarially Robust Deepfake Detection via Adversarial Feature Similarity Learning
By optimizing the similarity between samples and weight vectors, our approach aims to distinguish between real and fake instances.
Towards mitigating uncann(eye)ness in face swaps via gaze-centric loss terms
We additionally propose a novel loss equation for the training of face swapping models, leveraging a pretrained gaze estimation network to directly improve representation of the eyes.
GenFace: A Large-Scale Fine-Grained Face Forgery Benchmark and Cross Appearance-Edge Learning
In this paper, we propose a large-scale, diverse, and fine-grained high-fidelity dataset, namely GenFace, to facilitate the advancement of deepfake detection, which contains a large number of forgery faces generated by advanced generators such as the diffusion-based model and more detailed labels about the manipulation approaches and adopted generators.
Masked Conditional Diffusion Model for Enhancing Deepfake Detection
this paper present we put a new insight into diffusion model-based data augmentation, and propose a Masked Conditional Diffusion Model (MCDM) for enhancing deepfake detection.
A novel approach for detecting deep fake videos using graph neural network
The research gap lies in the fact that traditional deep fake detection methods, such as visual quality analysis or inconsistency detection, need help to keep up with the rapidly advancing technology used to create deep fakes.
Common Sense Reasoning for Deep Fake Detection
State-of-the-art approaches rely on image-based features extracted via neural networks for the deepfake detection binary classification.
Delocate: Detection and Localization for Deepfake Videos with Randomly-Located Tampered Traces
Deepfake videos are becoming increasingly realistic, showing subtle tampering traces on facial areasthat vary between frames.
