Diffusion-Based Audio Purification for Adversarial Deepfake Detection
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Solution Overview
Problem
Conventional deepfake detection methods are ineffective against sophisticated adversarial attacks, as they fail to adequately identify and remove adversarial noise, leading to false positives and negatives in audio signal authentication.
Innovation Solution
A machine-learning architecture incorporating a diffusion-based purification pipeline that preprocesses audio signals to remove adversarial noise, followed by a deepfake detection model to generate a spoofing score, enhancing the robustness of deepfake detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional deepfake detection methods are used, then the system can process audio signals quickly, but the detection accuracy deteriorates under adversarial attacks
Solution Approach 1:
The diffusion-based purification pipeline is applied as a preprocessing step before the audio signal reaches the deepfake detection model. This preliminary action removes adversarial noise and artifacts from the audio signal, ensuring that the detection model receives clean input data, thereby maintaining high detection accuracy even under adversarial attacks
Solution Approach 2:
The diffusion model acts as an intermediary component between the audio signal and the deepfake detection model. It purifies the audio signal by removing adversarial noise while preserving the underlying speech content, enabling the detection model to operate on cleaned data and achieve reliable detection results
2Measurement precision
If sophisticated deep learning models are used for anti-spoofing, then detection capability improves, but vulnerability to adversarial noise increases
Solution Approach 1:
The diffusion-based purification pipeline converts the harmful adversarial noise into benefit by using the noise characteristics as input for the diffusion model. The model learns to identify and remove these adversarial patterns, transforming the previously harmful noise into an opportunity for purification and enhanced detection robustness
3Reliability
If audio signals are processed without purification, then processing speed is maintained, but detection reliability under attack deteriorates
Solution Approach 1:
The system architecture is segmented into distinct functional modules: the diffusion-based purification pipeline as a separate preprocessing module, and the deepfake detection model as a separate analysis module. This segmentation allows each module to specialize in its function while maintaining overall system manageability and reliability
Data Source
AI summary
Disclosed are systems and methods including software processes executed by a server that detect audio-based synthetic speech (“deepfakes”). Embodiments implement a machine-learning architecture having a diffusion model that generates purified features that are fed to a deepfake detection model. The machine-learning architecture includes input layers that convert an audio signal into a Gaussian or frequency space representation (e.g., log spectrogram) to extract a set of initial features indicative of spoofing or deepfake attacks. The diffusion model identifies adversarial noise on the audio signal in the initial features and generates purified features or clean version of the input audio signal. A deepfake detector includes a neural network architecture and classifier programmed and trained to generate a deepfake detection score and classify the audio signal as genuine or fraudulent using the purified features.