Robust Audio Splicing Detection and Localization with Transformer Networks

Core Concepts

A Transformer sequence-to-sequence network that can robustly detect and localize single and multiple audio splices under various post-processing operations, outperforming existing dedicated and general-purpose approaches.

Abstract

The paper proposes a Transformer sequence-to-sequence (seq2seq) network for the task of audio splicing detection and localization. The method aims to address the need for more generally applicable techniques, as criminal investigators often face audio samples from unconstrained sources with unknown characteristics.
The authors simulate various attack scenarios in the form of post-processing operations that may disguise splicing, including MP3 and AMR-NB compression, additive synthetic and real noise, and splicing of samples from different/same and echoic/anechoic environments.
The proposed Transformer seq2seq network outperforms existing dedicated approaches for splicing detection as well as general-purpose networks like EfficientNet and RegNet. The method exhibits strong robustness and generalization abilities, particularly in challenging multi-splicing scenarios with diverse post-processing operations. It is more parameter-efficient than the baselines.
The authors perform extensive evaluations, including cross-dataset validation, robustness to noise and compression, and advanced multi-splicing forgery models. The results demonstrate the effectiveness and broad applicability of the proposed approach compared to prior work.

Stats

Audio samples can be spliced with 0 to 5 splicing points.
Post-processing operations include MP3 and AMR-NB compression, additive synthetic and real noise, and splicing of samples from different/same and echoic/anechoic environments.
The dataset consists of audio samples from the ACE and Hi-Fi TTS datasets, with disjoint speaker pools for training, validation, and testing.

Quotes

"Freely available and easy-to-use audio editing tools make it straightforward to perform audio splicing. Convincing forgeries can be created by combining various speech samples from the same person."
"With this work, we aim to take a first step towards unconstrained audio splicing detection to address this need."
"Our extensive evaluation shows that the proposed method outperforms existing dedicated approaches for splicing detection as well as the general-purpose networks EfficientNet and RegNet."

Key Insights Distilled From

Towards Unconstrained Audio Splicing Detection and Localization with Neural Networks

by Denise Mouss... at arxiv.org 05-06-2024

https://arxiv.org/pdf/2207.14682.pdf

Deeper Inquiries

How could the proposed Transformer seq2seq model be further improved to achieve even stronger robustness and generalization, particularly for the challenging intersplicing scenario

To enhance the robustness and generalization of the proposed Transformer seq2seq model, particularly for the challenging intersplicing scenario, several improvements can be considered:

Data Augmentation: Introduce more diverse and realistic intersplicing scenarios in the training data to expose the model to a wider range of variations. This can include different types of background noise, varying levels of compression, and additional environmental factors.

Regularization Techniques: Implement regularization methods such as dropout, batch normalization, or weight decay to prevent overfitting and improve the model's ability to generalize to unseen data.

Attention Mechanism Refinement: Fine-tune the attention mechanism in the Transformer architecture to focus on relevant audio features that are crucial for detecting intersplicing. This can help the model better capture long-range dependencies in the audio data.

Ensemble Learning: Combine multiple Transformer models trained with different hyperparameters or architectures to create an ensemble model. This can help improve the model's overall performance and robustness by leveraging diverse perspectives.

Transfer Learning: Pre-train the model on a larger and more diverse dataset related to audio forensics or similar tasks before fine-tuning it on the specific intersplicing detection task. Transfer learning can help the model learn more generalized audio features that are beneficial for challenging scenarios.

What other types of audio manipulation, beyond splicing, could be detected and localized using the proposed approach

The proposed Transformer seq2seq model can be adapted to detect and localize various other types of audio manipulation beyond splicing. Some potential audio manipulations that could be detected and localized using this approach include:

Audio Tampering: Detecting alterations such as pitch shifting, time stretching, or reverb effects applied to audio recordings.

Voice Morphing: Identifying instances where a speaker's voice has been morphed or altered to sound like a different individual.

Audio Forgery: Detecting fabricated audio content created through techniques like voice synthesis, voice conversion, or deepfake audio generation.

Background Noise Removal: Localizing and removing unwanted background noise or interference from audio recordings to enhance audio quality.

Audio Watermarking: Detecting hidden watermarks or signatures embedded in audio files for copyright protection or authentication purposes.

By training the Transformer model on diverse datasets containing examples of these audio manipulations, it can learn to recognize patterns and features indicative of each type of manipulation, enabling accurate detection and localization.

How could the insights from this work on audio forensics be applied to other multimedia modalities, such as image or video forensics

The insights gained from this work on audio forensics can be applied to other multimedia modalities, such as image or video forensics, in the following ways:

Deep Learning Models: Similar to the Transformer seq2seq model for audio splicing detection, deep learning architectures can be utilized for image and video forensics tasks. Models like convolutional neural networks (CNNs) and recurrent neural networks (RNNs) can be adapted to analyze and detect manipulations in images and videos.

Feature Extraction: Techniques used to extract features from audio data for splicing detection can be modified and applied to images and videos. Feature extraction methods like Mel spectrograms, MFCCs, and spectral centroid features can be used to capture unique characteristics of multimedia content.

Data Augmentation: Just as diverse scenarios were simulated for audio splicing detection, various manipulations and distortions can be introduced in image and video datasets to train models for detecting forgeries, alterations, or deepfake content.

Cross-Modal Learning: Explore cross-modal learning approaches where information from one modality (e.g., audio) is used to enhance the analysis and detection of manipulations in another modality (e.g., images or videos). This can improve the overall accuracy and reliability of multimedia forensics tasks.

By leveraging the methodologies and insights from audio forensics research, advancements can be made in the field of image and video forensics, contributing to improved detection and localization of manipulations in multimedia content.