HFMF: Hierarchical Fusion Meets Multi-Stream Models for Deepfake Detection

• URL: http://arxiv.org/abs/2501.05631v1
• Date: Fri, 10 Jan 2025 00:20:29 GMT
• Title: HFMF: Hierarchical Fusion Meets Multi-Stream Models for Deepfake Detection
• Authors: Anant Mehta, Bryant McArthur, Nagarjuna Kolloju, Zhengzhong Tu,
• Abstract summary: HFMF is a comprehensive two-stage deepfake detection framework. It integrates vision Transformers and convolutional nets through a hierarchical feature fusion mechanism. We demonstrate that our architecture achieves superior performance across diverse dataset benchmarks.
• Score: 4.908389661988192
• License:
• Abstract: The rapid progress in deep generative models has led to the creation of incredibly realistic synthetic images that are becoming increasingly difficult to distinguish from real-world data. The widespread use of Variational Models, Diffusion Models, and Generative Adversarial Networks has made it easier to generate convincing fake images and videos, which poses significant challenges for detecting and mitigating the spread of misinformation. As a result, developing effective methods for detecting AI-generated fakes has become a pressing concern. In our research, we propose HFMF, a comprehensive two-stage deepfake detection framework that leverages both hierarchical cross-modal feature fusion and multi-stream feature extraction to enhance detection performance against imagery produced by state-of-the-art generative AI models. The first component of our approach integrates vision Transformers and convolutional nets through a hierarchical feature fusion mechanism. The second component of our framework combines object-level information and a fine-tuned convolutional net model. We then fuse the outputs from both components via an ensemble deep neural net, enabling robust classification performances. We demonstrate that our architecture achieves superior performance across diverse dataset benchmarks while maintaining calibration and interoperability.

Related papers

• HyperDet: Generalizable Detection of Synthesized Images by Generating and Merging A Mixture of Hyper LoRAs [17.88153857572688]
  We introduce a novel and generalizable detection framework termed HyperDet. In this work, we propose a novel objective function that balances the pixel and semantic artifacts effectively. Our work paves a new way to establish generalizable domain-specific fake image detectors based on pretrained large vision models.
  arXiv  Detail & Related papers  (2024-10-08T13:43:01Z)
• Straight Through Gumbel Softmax Estimator based Bimodal Neural Architecture Search for Audio-Visual Deepfake Detection [6.367999777464464]
  multimodal deepfake detectors rely on conventional fusion methods, such as majority rule and ensemble voting. In this paper, we introduce the Straight-through Gumbel-Softmax framework, offering a comprehensive approach to search multimodal fusion model architectures. Experiments on the FakeAVCeleb and SWAN-DF datasets demonstrated an impressive AUC value 94.4% achieved with minimal model parameters.
  arXiv  Detail & Related papers  (2024-06-19T09:26:22Z)
• 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)
• DetDiffusion: Synergizing Generative and Perceptive Models for Enhanced Data Generation and Perception [78.26734070960886]
  Current perceptive models heavily depend on resource-intensive datasets. We introduce perception-aware loss (P.A. loss) through segmentation, improving both quality and controllability. Our method customizes data augmentation by extracting and utilizing perception-aware attribute (P.A. Attr) during generation.
  arXiv  Detail & Related papers  (2024-03-20T04:58:03Z)
• GenFace: A Large-Scale Fine-Grained Face Forgery Benchmark and Cross Appearance-Edge Learning [50.7702397913573]
  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)
• A Dual Attentive Generative Adversarial Network for Remote Sensing Image Change Detection [6.906936669510404]
  We propose a dual attentive generative adversarial network for achieving very high-resolution remote sensing image change detection tasks. The DAGAN framework has better performance with 85.01% mean IoU and 91.48% mean F1 score than advanced methods on the LEVIR dataset.
  arXiv  Detail & Related papers  (2023-10-03T08:26:27Z)
• 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)
• MMLatch: Bottom-up Top-down Fusion for Multimodal Sentiment Analysis [84.7287684402508]
  Current deep learning approaches for multimodal fusion rely on bottom-up fusion of high and mid-level latent modality representations. Models of human perception highlight the importance of top-down fusion, where high-level representations affect the way sensory inputs are perceived. We propose a neural architecture that captures top-down cross-modal interactions, using a feedback mechanism in the forward pass during network training.
  arXiv  Detail & Related papers  (2022-01-24T17:48:04Z)
• 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)
• High-Fidelity Synthesis with Disentangled Representation [60.19657080953252]
  We propose an Information-Distillation Generative Adrial Network (ID-GAN) for disentanglement learning and high-fidelity synthesis. Our method learns disentangled representation using VAE-based models, and distills the learned representation with an additional nuisance variable to the separate GAN-based generator for high-fidelity synthesis. Despite the simplicity, we show that the proposed method is highly effective, achieving comparable image generation quality to the state-of-the-art methods using the disentangled representation.
  arXiv  Detail & Related papers  (2020-01-13T14:39:40Z)

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.