Parents and Children: Distinguishing Multimodal DeepFakes from Natural Images
- URL: http://arxiv.org/abs/2304.00500v2
- Date: Tue, 21 May 2024 10:05:12 GMT
- Title: Parents and Children: Distinguishing Multimodal DeepFakes from Natural Images
- Authors: Roberto Amoroso, Davide Morelli, Marcella Cornia, Lorenzo Baraldi, Alberto Del Bimbo, Rita Cucchiara,
- Abstract summary: 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.
- Score: 60.34381768479834
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Recent advancements in diffusion models have enabled the generation of realistic deepfakes from textual prompts in natural language. While these models have numerous benefits across various sectors, they have also raised concerns about the potential misuse of fake images and cast new pressures on fake image detection. In this work, we pioneer a systematic study on deepfake detection generated by state-of-the-art diffusion models. Firstly, we conduct a comprehensive analysis of the performance of contrastive and classification-based visual features, respectively extracted from CLIP-based models and ResNet or ViT-based architectures trained on image classification datasets. Our results demonstrate that fake images share common low-level cues, which render them easily recognizable. Further, we devise a multimodal setting wherein fake images are synthesized by different textual captions, which are used as seeds for a generator. Under this setting, we quantify the performance of fake detection strategies and introduce a contrastive-based disentangling method that lets us analyze the role of the semantics of textual descriptions and low-level perceptual cues. Finally, we release a new dataset, called COCOFake, containing about 1.2M images generated from the original COCO image-caption pairs using two recent text-to-image diffusion models, namely Stable Diffusion v1.4 and v2.0.
Related papers
- DILLEMA: Diffusion and Large Language Models for Multi-Modal Augmentation [0.13124513975412253]
We present a novel framework for testing vision neural networks that leverages Large Language Models and control-conditioned Diffusion Models.
Our approach begins by translating images into detailed textual descriptions using a captioning model.
These descriptions are then used to produce new test images through a text-to-image diffusion process.
arXiv Detail & Related papers (2025-02-05T16:35:42Z) - Understanding and Improving Training-Free AI-Generated Image Detections with Vision Foundation Models [68.90917438865078]
Deepfake techniques for facial synthesis and editing pose serious risks for generative models.
In this paper, we investigate how detection performance varies across model backbones, types, and datasets.
We introduce Contrastive Blur, which enhances performance on facial images, and MINDER, which addresses noise type bias, balancing performance across domains.
arXiv Detail & Related papers (2024-11-28T13:04:45Z) - Contrasting Deepfakes Diffusion via Contrastive Learning and Global-Local Similarities [88.398085358514]
Contrastive Deepfake Embeddings (CoDE) is a novel embedding space specifically designed for deepfake detection.
CoDE is trained via contrastive learning by additionally enforcing global-local similarities.
arXiv Detail & Related papers (2024-07-29T18:00:10Z) - Robust CLIP-Based Detector for Exposing Diffusion Model-Generated Images [13.089550724738436]
Diffusion models (DMs) have revolutionized image generation, producing high-quality images with applications spanning various fields.
Their ability to create hyper-realistic images poses significant challenges in distinguishing between real and synthetic content.
This work introduces a robust detection framework that integrates image and text features extracted by CLIP model with a Multilayer Perceptron (MLP) classifier.
arXiv Detail & Related papers (2024-04-19T14:30:41Z) - ASAP: Interpretable Analysis and Summarization of AI-generated Image Patterns at Scale [20.12991230544801]
Generative image models have emerged as a promising technology to produce realistic images.
There is growing demand to empower users to effectively discern and comprehend patterns of AI-generated images.
We develop ASAP, an interactive visualization system that automatically extracts distinct patterns of AI-generated images.
arXiv Detail & Related papers (2024-04-03T18:20:41Z) - Enhance Image Classification via Inter-Class Image Mixup with Diffusion Model [80.61157097223058]
A prevalent strategy to bolster image classification performance is through augmenting the training set with synthetic images generated by T2I models.
In this study, we scrutinize the shortcomings of both current generative and conventional data augmentation techniques.
We introduce an innovative inter-class data augmentation method known as Diff-Mix, which enriches the dataset by performing image translations between classes.
arXiv Detail & Related papers (2024-03-28T17:23:45Z) - Improving Diffusion Models for Authentic Virtual Try-on in the Wild [53.96244595495942]
This paper considers image-based virtual try-on, which renders an image of a person wearing a curated garment.
We propose a novel diffusion model that improves garment fidelity and generates authentic virtual try-on images.
We present a customization method using a pair of person-garment images, which significantly improves fidelity and authenticity.
arXiv Detail & Related papers (2024-03-08T08:12:18Z) - Diversified in-domain synthesis with efficient fine-tuning for few-shot
classification [64.86872227580866]
Few-shot image classification aims to learn an image classifier using only a small set of labeled examples per class.
We propose DISEF, a novel approach which addresses the generalization challenge in few-shot learning using synthetic data.
We validate our method in ten different benchmarks, consistently outperforming baselines and establishing a new state-of-the-art for few-shot classification.
arXiv Detail & Related papers (2023-12-05T17:18:09Z) - Generalizable Synthetic Image Detection via Language-guided Contrastive
Learning [22.4158195581231]
malevolent use of synthetic images, such as the dissemination of fake news or the creation of fake profiles, raises significant concerns regarding the authenticity of images.
We propose a simple yet very effective synthetic image detection method via a language-guided contrastive learning and a new formulation of the detection problem.
It is shown that our proposed LanguAge-guided SynThEsis Detection (LASTED) model achieves much improved generalizability to unseen image generation models.
arXiv Detail & Related papers (2023-05-23T08:13:27Z) - DE-FAKE: Detection and Attribution of Fake Images Generated by
Text-to-Image Diffusion Models [12.310393737912412]
We pioneer a systematic study of the authenticity of fake images generated by text-to-image diffusion models.
For visual modality, we propose universal detection that demonstrates fake images of these text-to-image diffusion models share common cues.
For linguistic modality, we analyze the impacts of text captions on the image authenticity of text-to-image diffusion models.
arXiv Detail & Related papers (2022-10-13T13:08:54Z)
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.