Level Up the Deepfake Detection: a Method to Effectively Discriminate
Images Generated by GAN Architectures and Diffusion Models
- URL: http://arxiv.org/abs/2303.00608v1
- Date: Wed, 1 Mar 2023 16:01:46 GMT
- Title: Level Up the Deepfake Detection: a Method to Effectively Discriminate
Images Generated by GAN Architectures and Diffusion Models
- Authors: Luca Guarnera (1), Oliver Giudice (2), Sebastiano Battiato (1) ((1)
Department of Mathematics and Computer Science, University of Catania, Italy,
(2) Applied Research Team, IT dept., Banca d'Italia, Rome, Italy)
- Abstract summary: The deepfake detection and recognition task was investigated by collecting a dedicated dataset of pristine images and fake ones.
A hierarchical multi-level approach was introduced to solve three different deepfake detection and recognition tasks.
Experimental results demonstrated, in each case, more than 97% classification accuracy, outperforming state-of-the-art methods.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: The image deepfake detection task has been greatly addressed by the
scientific community to discriminate real images from those generated by
Artificial Intelligence (AI) models: a binary classification task. In this
work, the deepfake detection and recognition task was investigated by
collecting a dedicated dataset of pristine images and fake ones generated by 9
different Generative Adversarial Network (GAN) architectures and by 4
additional Diffusion Models (DM). A hierarchical multi-level approach was then
introduced to solve three different deepfake detection and recognition tasks:
(i) Real Vs AI generated; (ii) GANs Vs DMs; (iii) AI specific architecture
recognition. Experimental results demonstrated, in each case, more than 97%
classification accuracy, outperforming state-of-the-art methods.
Related papers
- DA-HFNet: Progressive Fine-Grained Forgery Image Detection and Localization Based on Dual Attention [12.36906630199689]
We construct a DA-HFNet forged image dataset guided by text or image-assisted GAN and Diffusion model.
Our goal is to utilize a hierarchical progressive network to capture forged artifacts at different scales for detection and localization.
arXiv Detail & Related papers (2024-06-03T16:13:33Z) - RIGID: A Training-free and Model-Agnostic Framework for Robust AI-Generated Image Detection [60.960988614701414]
RIGID is a training-free and model-agnostic method for robust AI-generated image detection.
RIGID significantly outperforms existing trainingbased and training-free detectors.
arXiv Detail & Related papers (2024-05-30T14:49:54Z) - GenFace: A Large-Scale Fine-Grained Face Forgery Benchmark and Cross
Appearance-Edge Learning [49.93362169016503]
The rapid advancement of photorealistic generators has reached a critical juncture where the discrepancy between authentic and manipulated images is increasingly indistinguishable.
Although there have been a number of publicly available face forgery datasets, the forgery faces are mostly generated using GAN-based synthesis technology.
We propose a large-scale, diverse, and fine-grained high-fidelity dataset, namely GenFace, to facilitate the advancement of deepfake detection.
arXiv Detail & Related papers (2024-02-03T03:13:50Z) - DiAD: A Diffusion-based Framework for Multi-class Anomaly Detection [55.48770333927732]
We propose a Difusion-based Anomaly Detection (DiAD) framework for multi-class anomaly detection.
It consists of a pixel-space autoencoder, a latent-space Semantic-Guided (SG) network with a connection to the stable diffusion's denoising network, and a feature-space pre-trained feature extractor.
Experiments on MVTec-AD and VisA datasets demonstrate the effectiveness of our approach.
arXiv Detail & Related papers (2023-12-11T18:38:28Z) - DeepFidelity: Perceptual Forgery Fidelity Assessment for Deepfake
Detection [67.3143177137102]
Deepfake detection refers to detecting artificially generated or edited faces in images or videos.
We propose a novel Deepfake detection framework named DeepFidelity to adaptively distinguish real and fake faces.
arXiv Detail & Related papers (2023-12-07T07:19:45Z) - Parents and Children: Distinguishing Multimodal DeepFakes from Natural Images [60.34381768479834]
Recent advancements in diffusion models have enabled the generation of realistic deepfakes from textual prompts in natural language.
We pioneer a systematic study on deepfake detection generated by state-of-the-art diffusion models.
arXiv Detail & Related papers (2023-04-02T10:25:09Z) - GLFF: Global and Local Feature Fusion for AI-synthesized Image Detection [29.118321046339656]
We propose a framework to learn rich and discriminative representations by combining multi-scale global features from the whole image with refined local features from informative patches for AI synthesized image detection.
GLFF fuses information from two branches: the global branch to extract multi-scale semantic features and the local branch to select informative patches for detailed local artifacts extraction.
arXiv Detail & Related papers (2022-11-16T02:03:20Z) - On the Exploitation of Deepfake Model Recognition [0.0]
The recognition of a specific GAN model that generated the deepfake image is a task not yet completely addressed in the state-of-the-art.
A robust processing pipeline to evaluate the possibility to point-out analytic fingerprints for Deepfake model recognition is presented.
The study takes an important step in countering the Deepfake phenomenon introducing a sort of signature in some sense similar to those employed in the multimedia forensics field.
arXiv Detail & Related papers (2022-04-09T16:48:23Z) - M2TR: Multi-modal Multi-scale Transformers for Deepfake Detection [74.19291916812921]
forged images generated by Deepfake techniques pose a serious threat to the trustworthiness of digital information.
In this paper, we aim to capture the subtle manipulation artifacts at different scales for Deepfake detection.
We introduce a high-quality Deepfake dataset, SR-DF, which consists of 4,000 DeepFake videos generated by state-of-the-art face swapping and facial reenactment methods.
arXiv Detail & Related papers (2021-04-20T05:43:44Z) - Fighting deepfakes by detecting GAN DCT anomalies [0.0]
State-of-the-art algorithms employ deep neural networks to detect fake contents.
A new fast detection method able to discriminate Deepfake images with high precision is proposed.
The method is innovative, exceeds the state-of-the-art and also gives many insights in terms of explainability.
arXiv Detail & Related papers (2021-01-24T19:45:11Z) - DeepFake Detection by Analyzing Convolutional Traces [0.0]
We focus on the analysis of Deepfakes of human faces with the objective of creating a new detection method.
The proposed technique, by means of an Expectation Maximization (EM) algorithm, extracts a set of local features specifically addressed to model the underlying convolutional generative process.
Results demonstrated the effectiveness of the technique in distinguishing the different architectures and the corresponding generation process.
arXiv Detail & Related papers (2020-04-22T09:02:55Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.