DeepFake Detection
125 papers with code • 5 benchmarks • 16 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
Libraries
Use these libraries to find DeepFake Detection models and implementationsDatasets
Most implemented papers
Video Face Manipulation Detection Through Ensemble of CNNs
In this paper, we tackle the problem of face manipulation detection in video sequences targeting modern facial manipulation techniques.
FakeAVCeleb: A Novel Audio-Video Multimodal Deepfake Dataset
We generate this dataset using the most popular deepfake generation methods.
Explaining deep learning models for spoofing and deepfake detection with SHapley Additive exPlanations
Substantial progress in spoofing and deepfake detection has been made in recent years.
WaveFake: A Data Set to Facilitate Audio Deepfake Detection
Deep generative modeling has the potential to cause significant harm to society.
ADD: Frequency Attention and Multi-View based Knowledge Distillation to Detect Low-Quality Compressed Deepfake Images
In particular, we propose the Attention-based Deepfake detection Distiller (ADD), which consists of two novel distillations: 1) frequency attention distillation that effectively retrieves the removed high-frequency components in the student network, and 2) multi-view attention distillation that creates multiple attention vectors by slicing the teacher's and student's tensors under different views to transfer the teacher tensor's distribution to the student more efficiently.
Cross-Forgery Analysis of Vision Transformers and CNNs for Deepfake Image Detection
Deepfake Generation Techniques are evolving at a rapid pace, making it possible to create realistic manipulated images and videos and endangering the serenity of modern society.
Masked Relation Learning for DeepFake Detection
A relation learning module masks partial correlations between regions to reduce redundancy and then propagates the relational information across regions to capture the irregularity from a global view of the graph.
PTW: Pivotal Tuning Watermarking for Pre-Trained Image Generators
We propose an adaptive attack that can successfully remove any watermarking with access to only 200 non-watermarked images.
Undercover Deepfakes: Detecting Fake Segments in Videos
This paradigm has been under-explored by the current deepfake detection methods in the academic literature.
DeepFidelity: Perceptual Forgery Fidelity Assessment for Deepfake Detection
Considering the complexity of the quality distribution of both real and fake faces, we propose a novel Deepfake detection framework named DeepFidelity to adaptively distinguish real and fake faces with varying image quality by mining the perceptual forgery fidelity of face images.