Neural Network Audio Watermark Detector for Deepfake Identification
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Solution Overview
Problem
Conventional audio watermarking techniques are inadequate in detecting audio watermarks in degraded audio waveforms, particularly against modern attacks such as pitch shifting, added reverb, time-stretching, denoising, and re-recording, and are ill-suited to handle deepfake audio forgeries, which can minimize degradation and evade detection.
Innovation Solution
An audio watermark detector using a neural network, specifically a convolutional neural network performing 1D convolutions on time domain samples from chunks of audio, is trained to detect the presence of an audio watermark embedded using a particular technique, even in degraded audio, and can be part of a generative adversarial network to enhance robustness against neural network-based attacks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional audio watermarking techniques are used, then the watermark can be embedded in audio, but the detection accuracy degrades significantly when the audio is subjected to modern attacks such as pitch shifting, added reverb, time-stretching, denoising, and re-recording
Solution Approach 1:
The neural network detector is trained in advance on a diverse dataset that includes audio samples subjected to various attacks and degradations. This preliminary training enables the detector to learn robust features that persist even after attacks, allowing it to maintain high detection accuracy when confronted with degraded audio during actual operation.
Solution Approach 2:
The system transforms the detection approach by changing from traditional signal processing methods to neural network-based detection. The neural network learns optimal feature representations and detection parameters automatically from training data, adapting to various attack conditions without requiring manual parameter tuning for each attack type.
2Stability of the object's composition
If audio watermarks are made imperceptible to maintain audio quality, then the watermark becomes harder to detect in degraded audio, reducing detection robustness
Solution Approach 1:
The patent replaces traditional mechanical signal processing-based detection methods with a neural network-based detection system. The neural network automatically learns to extract watermark features from imperceptible watermarks in degraded audio, achieving both high detection precision and audio quality preservation without the trade-offs of conventional methods.
Solution Approach 2:
The neural network acts as an intermediary that bridges the gap between imperceptible watermarks and detectable features. It learns to recognize subtle patterns in the audio signal that correspond to watermarks even when they are masked by degradation, effectively translating imperceptible changes into reliable detection decisions.
3Reliability
If traditional watermarking embedding techniques are used, then the watermark can be embedded, but it cannot withstand neural network-based attacks and deepfake generation
Solution Approach 1:
The neural network detector is trained in advance on a diverse dataset that includes audio samples subjected to various attacks and degradations. This preliminary training enables the detector to learn robust features that persist even after attacks, allowing it to maintain high detection accuracy when confronted with degraded audio during actual operation.
Solution Approach 2:
The neural network-based detector is designed to be universally applicable against multiple types of attacks and degradations. Rather than requiring separate detection methods for each attack type, the single neural network model learns to handle pitch shifting, reverb, time-stretching, denoising, re-recording, and deepfake generation simultaneously, providing versatile protection.
Data Source
AI summary
Embodiments provide systems, methods, and computer storage media for secure audio watermarking and audio authenticity verification. An audio watermark detector may include a neural network trained to detect a particular audio watermark and embedding technique, which may indicate source software used in a workflow that generated an audio file under test. For example, the watermark may indicate an audio file was generated using voice manipulation software, so detecting the watermark can indicate manipulated audio such as deepfake audio and other attacked audio signals. In some embodiments, the audio watermark detector may be trained as part of a generative adversarial network in order to make the underlying audio watermark more robust to neural network-based attacks. Generally, the audio watermark detector may evaluate time domain samples from chunks of an audio clip under test to detect the presence of the audio watermark and generate a classification for the audio clip.