Graph Fingerprinting for Adversarial Attack Detection in GNNs
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Graph neural networks (GNNs) are vulnerable to adversarial attacks that modify graph data structures, leading to incorrect predictions and classifications, which can have catastrophic consequences in applications like automated vehicle controls and patient medical treatments.
Innovation Solution
Implement a computing tool that uses graph fingerprinting to detect adversarial attacks by comparing structural and learning-based fingerprints of graph data structures, utilizing hashing and GNN performance to identify tampering.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If graph neural networks are used for predictions and classifications, then the system can process complex graph data structures, but the system becomes vulnerable to adversarial attacks that modify graph data structures
Solution Approach 1:
The patent applies preliminary action by generating fingerprints of the original graph data structure before it is potentially attacked. These fingerprints serve as reference markers that enable later detection of any modifications. The fingerprint generation occurs in advance during normal operation, creating a baseline against which future graph structures can be compared to detect adversarial changes.
Solution Approach 2:
The patent introduces fingerprints as an intermediary element between the graph data structure and the GNN processing. These fingerprints act as mediators that carry structural information about the graph without being the graph itself. By comparing fingerprints rather than directly analyzing the entire graph structure, the system can detect adversarial modifications while maintaining the ability to process complex graph data.
2Reliability
If the system detects adversarial attacks by comparing graph structures, then the reliability of GNN outputs is improved, but the complexity of the detection mechanism increases
Solution Approach 1:
The patent applies the extraction principle by separating the detection function from the main GNN processing. Instead of making the entire GNN complex and adversarial-resistant, the system extracts a simplified fingerprint representation of the graph structure that can be quickly compared. This extraction allows the main GNN to remain relatively simple while the fingerprint comparison provides the reliability enhancement.
Solution Approach 2:
The patent uses copying by creating fingerprint copies of the original graph structure. Rather than storing and comparing entire graph structures (which would be computationally expensive), the system creates simplified copy representations (fingerprints) that capture essential structural features. These copies enable efficient comparison while reducing the complexity burden on the detection mechanism.
3Reliability
If fingerprints are generated and compared to detect attacks, then the system can identify tampering, but additional processing time and computational resources are required
Solution Approach 1:
The patent applies local quality by making the fingerprint generation and comparison process localized and targeted rather than global and exhaustive. The fingerprints focus on specific structural features of the graph that are most indicative of adversarial modifications. This localized approach allows the system to achieve reliable detection without the need to process and compare every aspect of the graph structure, thereby reducing overall processing time.
Data Source
AI summary
Mechanisms are provided for detecting adversarial attacks on graph data structures. A first graph fingerprint engine generates, for a first graph data structure, a first fingerprint data structure-based on features extracted from the first graph data structure. A second graph data structure is received, and a second graph fingerprint engine generates a second graph fingerprint data structure based on features extracted from the second graph data structure. An adversarial attack detection engine compares the first fingerprint data structure to the second fingerprint data structure to determine whether the first fingerprint data structure matches the second fingerprint data structure. In response to the first fingerprint data structure not matching the second fingerprint data structure, the adversarial attack detection engine outputs an output indicating that the second data structure corresponds to an adversarial attack.