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
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Latest papers with no code
Exploring Green AI for Audio Deepfake Detection
In contrast to existing methods that fine-tune SSL models and employ additional deep neural networks for downstream tasks, we exploit classical machine learning algorithms such as logistic regression and shallow neural networks using the SSL embeddings extracted using the pre-trained model.
Deepfake Detection without Deepfakes: Generalization via Synthetic Frequency Patterns Injection
We propose to train detectors using only pristine images injecting in part of them crafted frequency patterns, simulating the effects of various deepfake generation techniques without being specific to any.
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.
CLIPping the Deception: Adapting Vision-Language Models for Universal Deepfake Detection
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.
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.