Cross-Modal Video Forgery Detection Using Audio-Visual Embeddings
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Solution Overview
Problem
Existing techniques for detecting AI-generated videos often fail to identify fakes due to their focus on single modalities and reliance on supervised learning, which limits their ability to capture unseen audio-visual correspondences.
Innovation Solution
A machine-learning-based approach using a visual encoder, audio encoder, audio-to-visual network, and visual-to-audio network is trained to generate cross-modal representations by replacing subsets of image and audio embeddings, capturing intrinsic audio-visual correspondences in real videos and exploiting differences in cohesion to distinguish real from fake videos.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing techniques focus on single modality data (only audio or only visual), then processing requirements are reduced, but detection accuracy decreases because they cannot identify videos with fake data of one modality but real data of the other modality
Solution Approach 1:
The patent merges audio and visual modalities into a unified detection framework. The model simultaneously processes both audio and visual data through separate encoders (audio encoder and visual encoder) that generate embeddings, then combines these embeddings in a fusion module to make detection decisions. This allows the system to detect fake videos regardless of which modality is manipulated, achieving high detection accuracy across multiple fake video types.
Solution Approach 2:
The detection model is designed with multi-functionality to handle various types of fake videos (fake audio only, fake visual only, or both fake). The universal architecture uses modality-specific encoders followed by a fusion mechanism that can process any combination of real and fake data, making the system adaptable to different manipulation scenarios without requiring separate specialized models for each type.
2Adaptability or versatility
If supervised learning is used to train models to classify videos as real or fake, then training data requirements are reduced, but the models overlook correspondences that can help detect unseen AI-generated videos
Solution Approach 1:
The patent applies preliminary action through pre-training the audio and visual encoders on large datasets of real audio-visual data before fine-tuning on fake video detection. This pre-training stage allows the model to learn fundamental audio-visual correspondences and temporal patterns from authentic data, establishing a strong foundation that enables the model to detect unseen manipulation techniques without relying solely on supervised learning from labeled fake examples.
Solution Approach 2:
The patent introduces an intermediary fusion module that processes and compares audio and visual embeddings to detect inconsistencies. This intermediary component analyzes the correspondence between audio and visual modalities, identifying mismatches that indicate fake content. The fusion module acts as a mediator that combines information from both modalities and generates detection predictions, preserving critical audio-visual correspondence information throughout the processing pipeline.
Data Source
AI summary
A method for training a model for classifying videos as real or fake can include generating image tiles and audio data segments from an input video, generating a sequence of image embeddings based on the image tiles using a visual encoder and a sequence of audio embeddings based on the audio data segments using an audio encoder, transforming, using a V2A network, a first subset of the sequence of image embeddings into synthetic audio embeddings, transforming, using an A2V network, a first subset of the sequence of audio embeddings into synthetic image embeddings, updating the sequence of image embeddings by using the synthetic image embeddings, updating the sequence of audio embeddings using the synthetic audio embeddings, training the encoders and the networks using the updated sequences of image embeddings and audio embeddings, and training a classifier using the trained encoders and the trained networks.