Context and Transcripts Improve Detection of Deepfake Audios of Public Figures
Abstract
Humans use context to assess the veracity of information. However, current audio deepfake detectors only analyze the audio file without considering either context or transcripts. We create and analyze a Journalist-provided Deepfake Dataset (JDD) of 255 public deepfakes which were primarily contributed by over 70 journalists since early 2024. We also generate a synthetic audio dataset (SYN) of dead public figures and propose a novel Context-based Audio Deepfake Detector (CADD) architecture. In addition, we evaluate performance on two large-scale datasets: ITW and P$^2$V. We show that sufficient context and/or the transcript can significantly improve the efficacy of audio deepfake detectors. Performance (measured via F1 score, AUC, and EER) of multiple baseline audio deepfake detectors and traditional classifiers can be improved by 5%-37.58% in F1-score, 3.77%-42.79% in AUC, and 6.17%-47.83% in EER. We additionally show that CADD, via its use of context and/or transcripts, is more robust to 5 adversarial evasion strategies, limiting performance degradation to an average of just -0.71% across all experiments. Code, models, and datasets are available at our project page: https://sites.northwestern.edu/nsail/cadd-context-based-audio-deepfake-detection (access restricted during review).
The following analysis is generated by AI and may contain errors. It is separate from the authors’ abstract and is not peer review.
Research summary
The main contribution of this paper is the introduction of a novel architecture (CADD) that effectively integrates context and transcripts into audio deepfake detection, demonstrating significant improvements in performance and robustness against manipulations. This work represents a meaningful advancement in the fight against misinformation, particularly in the context of public figures, and sets a precedent for future research in the domain.
Methodology
The proposed Context-based Audio Deepfake Detector (CADD) architecture is innovative as it integrates contextual information and transcripts into the detection process, moving beyond traditional audio-only analysis. The methodology is well-structured, utilizing a combination of feature extraction from audio and contextual data, which is a significant departure from existing approaches that focus solely on audio signals. The use of a Journalist-provided Deepfake Dataset (JDD) adds real-world relevance to the methodology, enhancing its applicability in practical scenarios. The architecture's design allows it to build on existing audio deepfake detectors, which is a clever way to leverage prior work while introducing novel elements.
Results & evaluation
The experiments conducted are comprehensive, evaluating CADD against a substantial number of baselines (71 in total) across multiple datasets (JDD, SYN, ITW, and P$^2$V). The results demonstrate significant improvements in detection accuracy, with statistical significance established through rigorous hypothesis testing. The performance metrics (F1 score, AUC, EER) are well-defined, and the evaluation of robustness against adversarial manipulations adds depth to the experimental assessment. However, the paper could benefit from clearer visualizations and more detailed explanations of the results.
Reproducibility
The paper mentions that code, models, and datasets are available on a project page, which is a positive aspect for reproducibility. However, the specifics of the implementation details and the exact configurations used in experiments are not fully detailed in the main text, which may hinder complete reproducibility by other researchers. The supplementary materials could provide more clarity on the experimental setup.
Limitations
The paper acknowledges limitations, particularly in the datasets used. The JDD dataset, while valuable, is limited to public figures and may not represent the full spectrum of deepfake scenarios. Additionally, the reliance on simulated context in some datasets (like ITW) may not fully capture the complexities of real-world applications. The focus on deceased public figures in the SYN dataset also raises ethical concerns and limits applicability to living individuals.
Broader impact
The implications of this research are significant, particularly in the context of misinformation and the integrity of media. The ability to detect deepfake audios of public figures has far-reaching consequences for public trust and the prevention of fraud. The methodology could be adapted for various applications beyond audio, including video and image deepfake detection, which further enhances its potential impact. The main contribution of this paper is the introduction of a novel architecture (CADD) that effectively integrates context and transcripts into audio deepfake detection, demonstrating significant improvements in performance and robustness against manipulations. This work represents a meaningful advancement in the fight against misinformation, particularly in the context of public figures, and sets a precedent for future research in the domain.
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