Dual Neck Autoencoder Decorrelation for Adversarial Robustness
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Solution Overview
Problem
Deep learning models are vulnerable to adversarial attacks due to high linear correlation between feature sets extracted by different neural networks, leading to transferability of attacks between models, which raises security concerns and questions the true learning of desired features.
Innovation Solution
A dual neck autoencoder module is introduced, featuring an encoder and decoder with two bottleneck modules in parallel, trained using a correlation loss function to decorrelate the feature sets, reducing adversarial attack transferability by creating semantically different encodings.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If deep learning models use standard training methods, then they achieve high accuracy on natural samples, but they become vulnerable to adversarial attacks due to high linear correlation between feature sets
Solution Approach 1:
The patent divides the single feature extraction path into two parallel bottleneck modules that process features independently. This segmentation creates diverse feature representations that are less correlated, thereby reducing adversarial attack transferability while maintaining classification accuracy on natural images
Solution Approach 2:
The patent introduces asymmetric processing by using two different bottleneck modules with potentially different architectures or training dynamics. This asymmetry ensures that the feature sets extracted by each bottleneck are not linearly correlated, making it harder for adversarial attacks to transfer between models
2Reliability
If models are trained to improve robustness against adversarial attacks, then attack transferability reduces, but this may compromise performance on natural samples
Solution Approach 1:
The dual bottleneck architecture serves multiple functions simultaneously: each bottleneck independently extracts features for robust classification while their combined outputs provide resistance to adversarial attacks. This multi-functionality allows the system to maintain high accuracy on natural samples while achieving robustness without sacrificing one for the other
3Device complexity
If single bottleneck autoencoder is used, then device complexity is low, but adversarial attack transferability is high
Solution Approach 1:
The patent transitions from a single-dimensional feature extraction path to a two-dimensional parallel structure with two bottleneck modules. This dimensional change increases architectural complexity but effectively reduces adversarial attack transferability by creating less correlated feature sets through independent processing paths
Data Source
AI summary
In one embodiment, there is provided a dual neck autoencoder module for reducing adversarial attack transferability. The dual neck autoencoder module includes an encoder module configured to receive input data; a decoder module; and a first bottleneck module and a second bottleneck module coupled, in parallel, between the encoder module and the decoder module. The decoder module is configured to generate a first estimate based, at least in part, on a first intermediate data set from the first bottleneck module, and a second estimate based, at least in part, on a second intermediate data set from the second bottleneck module. The first intermediate data set and the second intermediate data set are at least partially decorrelated based, at least in part, on a correlation loss.