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 implementations

Most implemented papers

Video Face Manipulation Detection Through Ensemble of CNNs

polimi-ispl/icpr2020dfdc 16 Apr 2020

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

dash-lab/fakeavceleb 11 Aug 2021

We generate this dataset using the most popular deepfake generation methods.

Explaining deep learning models for spoofing and deepfake detection with SHapley Additive exPlanations

slundberg/shap 7 Oct 2021

Substantial progress in spoofing and deepfake detection has been made in recent years.

WaveFake: A Data Set to Facilitate Audio Deepfake Detection

rub-syssec/wavefake 4 Nov 2021

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

Leminhbinh0209/AAAI22-ADD 7 Dec 2021

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

davide-coccomini/Cross-Forgery-Analysis-of-Vision-Transformers-and-CNNs-for-Deepfake-Image-Detection 28 Jun 2022

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

zimyang/maskrelation 2023 2023

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

nilslukas/gan-watermark 14 Apr 2023

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

rgb91/temporal-deepfake-segmentation 11 May 2023

This paradigm has been under-explored by the current deepfake detection methods in the academic literature.

DeepFidelity: Perceptual Forgery Fidelity Assessment for Deepfake Detection

shimmer-ghq/deepfidelity 7 Dec 2023

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.