Multimodal AI Security Screening With Perceptual Distance Evaluation
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Solution Overview
Problem
Existing security protection methods for multimodal AI models are inadequate, as they fail to effectively evaluate the quality and similarity of generated data and lack comprehensive performance evaluation, leading to vulnerabilities and inconsistencies.
Innovation Solution
A systematic security protection method involving a distance-aware judgment model to assess perceptual distance, a multi-stream multimodal Transformer network for security encoding, tokenization and filtering, and omnidirectional comprehensive performance evaluation using correlation-based re-normalization to enhance security and reliability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing security protection methods are used for multimodal AI models, then the implementation is simple, but the security and reliability are insufficient
Solution Approach 1:
The patent divides the security protection system into multiple independent modules: distance-aware judgment model for data quality assessment, multi-stream multimodal Transformer network for security encoding, tokenization and filtering components, and omnidirectional evaluation system. Each module handles a specific aspect of security protection, allowing the complex system to be managed through modular components that can be developed and maintained independently while collectively providing comprehensive security.
Solution Approach 2:
The patent introduces a perceptual distance dimension to evaluate the similarity between generated data and original data, adding a new evaluation criterion beyond traditional security checks. The omnidirectional evaluation system also adds multiple evaluation dimensions (security, quality, performance) to assess the model comprehensively, transforming the security protection from a single-dimensional check to a multi-dimensional assessment framework.
2Measurement precision
If comprehensive performance evaluation is implemented, then the evaluation accuracy is improved, but the evaluation time and computational cost increase
Solution Approach 1:
The patent performs tokenization and filtering operations on the security coding sequence features before the main evaluation process. By pre-processing the data to remove potentially harmful tokens and organize the feature representations, the system reduces the computational burden during the actual omnidirectional evaluation, allowing comprehensive assessment without proportional increases in evaluation time.
Solution Approach 2:
The distance-aware judgment model evaluates only the perceptual distance between generated and original data, focusing on the most critical aspect of data quality. The omnidirectional evaluation system selectively assesses multiple dimensions but can prioritize key metrics based on specific security concerns, performing sufficient evaluation without necessarily completing every possible measurement, thus balancing accuracy with efficiency.
Data Source
AI summary
A systematic security protection method for a multimodal AI model is provided. The method comprises: in a security detection stage of the target multimodal AI model, according to a perceptual distance between detection data and original data determined by a trained distance-aware judgment model, obtaining detection data whose perceptual distance from the original data meets a requirement, and detecting the target multimodal AI model by using the detection data; in a case that the detection result in the detection stage is that the target multimodal AI model has a security problem, on one hand, performing security coding protection and tokenization filtering on the input of the model, on the other hand, performing multi-dimensional omnidirectional evaluation on the multimodal AI model.