Generalized Source Tracing: Detecting Novel Audio Deepfake Algorithm with Real Emphasis and Fake Dispersion Strategy

• URL: http://arxiv.org/abs/2406.03240v2
• Date: Sun, 9 Jun 2024 03:33:59 GMT
• Title: Generalized Source Tracing: Detecting Novel Audio Deepfake Algorithm with Real Emphasis and Fake Dispersion Strategy
• Authors: Yuankun Xie, Ruibo Fu, Zhengqi Wen, Zhiyong Wang, Xiaopeng Wang, Haonnan Cheng, Long Ye, Jianhua Tao,
• Abstract summary: We propose Real Emphasis and Fake Dispersion (REFD) strategy for audio deepfake algorithm recognition. REFD achieves 86.83% F1-score as a single system in Audio Deepfake Detection Challenge 2023 Track3, showcasing its state-of-the-art performance.
• Score: 39.93628750014384
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: With the proliferation of deepfake audio, there is an urgent need to investigate their attribution. Current source tracing methods can effectively distinguish in-distribution (ID) categories. However, the rapid evolution of deepfake algorithms poses a critical challenge in the accurate identification of out-of-distribution (OOD) novel deepfake algorithms. In this paper, we propose Real Emphasis and Fake Dispersion (REFD) strategy for audio deepfake algorithm recognition, demonstrating its effectiveness in discriminating ID samples while identifying OOD samples. For effective OOD detection, we first explore current post-hoc OOD methods and propose NSD, a novel OOD approach in identifying novel deepfake algorithms through the similarity consideration of both feature and logits scores. REFD achieves 86.83% F1-score as a single system in Audio Deepfake Detection Challenge 2023 Track3, showcasing its state-of-the-art performance.

Related papers

• PUDD: Towards Robust Multi-modal Prototype-based Deepfake Detection [3.824522034247845]
  We propose a Prototype-based Unified Framework for Deepfake Detection (PUDD) PUDD offers a detection system based on similarity, comparing input data against known prototypes for video classification and identifying potential deepfakes or previously unseen classes by analyzing drops in similarity. Our experiments reveal three key findings: (1) PUDD achieves an accuracy of 95.1% on Celeb-DF, outperforming state-of-the-art deepfake detection methods; (2) PUDD leverages image classification as the upstream task during training, demonstrating promising performance in both image classification and deepfake detection tasks during inference; and (3) PUDD requires only 2.7 seconds for retraining
  arXiv  Detail & Related papers  (2024-06-22T19:21:42Z)
• Faster Than Lies: Real-time Deepfake Detection using Binary Neural Networks [0.0]
  Deepfake detection aims to contrast the spread of deep-generated media that undermines trust in online content. We introduce a novel deepfake detection approach on images using Binary Neural Networks (BNNs) for fast inference with minimal accuracy loss.
  arXiv  Detail & Related papers  (2024-06-07T13:37:36Z)
• What to Remember: Self-Adaptive Continual Learning for Audio Deepfake Detection [53.063161380423715]
  Existing detection models have shown remarkable success in discriminating known deepfake audio, but struggle when encountering new attack types. We propose a continual learning approach called Radian Weight Modification (RWM) for audio deepfake detection.
  arXiv  Detail & Related papers  (2023-12-15T09:52:17Z)
• Facial Forgery-based Deepfake Detection using Fine-Grained Features [7.378937711027777]
  Facial forgery by deepfakes has caused major security risks and raised severe societal concerns. We formulate deepfake detection as a fine-grained classification problem and propose a new fine-grained solution to it. Our method is based on learning subtle and generalizable features by effectively suppressing background noise and learning discriminative features at various scales for deepfake detection.
  arXiv  Detail & Related papers  (2023-10-10T21:30:05Z)
• CrossDF: Improving Cross-Domain Deepfake Detection with Deep Information Decomposition [53.860796916196634]
  We propose a Deep Information Decomposition (DID) framework to enhance the performance of Cross-dataset Deepfake Detection (CrossDF) Unlike most existing deepfake detection methods, our framework prioritizes high-level semantic features over specific visual artifacts. It adaptively decomposes facial features into deepfake-related and irrelevant information, only using the intrinsic deepfake-related information for real/fake discrimination.
  arXiv  Detail & Related papers  (2023-09-30T12:30:25Z)
• Beyond AUROC & co. for evaluating out-of-distribution detection performance [50.88341818412508]
  Given their relevance for safe(r) AI, it is important to examine whether the basis for comparing OOD detection methods is consistent with practical needs. We propose a new metric - Area Under the Threshold Curve (AUTC), which explicitly penalizes poor separation between ID and OOD samples.
  arXiv  Detail & Related papers  (2023-06-26T12:51:32Z)
• Adaptive Fake Audio Detection with Low-Rank Model Squeezing [50.7916414913962]
  Traditional approaches, such as finetuning, are computationally intensive and pose a risk of impairing the acquired knowledge of known fake audio types. We introduce the concept of training low-rank adaptation matrices tailored specifically to the newly emerging fake audio types. Our approach offers several advantages, including reduced storage memory requirements and lower equal error rates.
  arXiv  Detail & Related papers  (2023-06-08T06:06:42Z)
• Aggregating Layers for Deepfake Detection [20.191456827448736]
  We consider the case where the network is trained on one Deepfake algorithm, and tested on Deepfakes generated by another algorithm. Our algorithm aggregates features extracted across all layers of one backbone network to detect a fake. We evaluate our approach on two domains of interest - Deepfake detection and Synthetic image detection, and find that we achieve SOTA results.
  arXiv  Detail & Related papers  (2022-10-11T14:29:47Z)
• Triggering Failures: Out-Of-Distribution detection by learning from local adversarial attacks in Semantic Segmentation [76.2621758731288]
  We tackle the detection of out-of-distribution (OOD) objects in semantic segmentation. Our main contribution is a new OOD detection architecture called ObsNet associated with a dedicated training scheme based on Local Adversarial Attacks (LAA) We show it obtains top performances both in speed and accuracy when compared to ten recent methods of the literature on three different datasets.
  arXiv  Detail & Related papers  (2021-08-03T17:09:56Z)

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.