Neural Network Classification Defensive Noise Generator
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Solution Overview
Problem
Neural networks are vulnerable to adversarial attacks and noise, leading to decreased classification accuracy when input data differs from training data, particularly in fields like image classification and speech recognition.
Innovation Solution
A processor-implemented classification method that generates defensive noise and combines it with input images to enhance robustness, using a defensive noise generator trained to reduce loss values and an adversarial noise generator trained to increase loss values, with a noise discriminator to determine noise application, thereby improving classification accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If neural network classification is performed without defensive noise, then classification speed is maintained, but classification accuracy decreases when input data contains adversarial noise or differs from training data
Solution Approach 1:
The defensive noise generator is trained during the preprocessing stage to generate noise that will be applied to input images before classification. This preliminary action prepares the system to handle adversarial noise and out-of-distribution data by pre-computing appropriate noise patterns based on the input characteristics, rather than attempting to correct issues during the classification process itself.
Solution Approach 2:
Defensive noise acts as an intermediary between the input image and the classifier. The noise is generated based on the input image characteristics and applied as a preprocessing step, serving as a mediator that protects the classifier from adversarial attacks and improves robustness without requiring modifications to the classifier architecture itself.
2Reliability
If defensive noise is generated and combined with input images, then robustness against adversarial attacks is improved, but processing time increases
Solution Approach 1:
The system applies defensive noise selectively based on the input image characteristics. The noise generation and application is performed only when necessary to achieve the desired robustness level, rather than uniformly applying full noise processing to all inputs. This partial action approach maintains robustness while reducing unnecessary processing time for benign inputs.
3Reliability
If adversarial noise generator and defensive noise generator are trained with total loss value, then classification accuracy under attack is improved, but training complexity increases
Solution Approach 1:
The training process uses a total loss value that incorporates feedback from both the adversarial noise generator and the defensive noise generator. The loss function measures the effectiveness of the defensive noise in counteracting adversarial attacks, and this feedback is used to iteratively improve both generators during training, creating a coordinated system that enhances robustness through learned interactions.
Data Source
AI summary
A method and apparatus for classification using a neural network. A classification apparatus includes at least one processor and a memory. The memory is configured to store a classifier and a preprocessor including a defensive noise generator. The at least one processor generates defensive noise from an input image through the defensive noise generator in the preprocessor, generates a combined image by combining the input image and the defensive noise, and outputs a classification result by inputting the combined image to the classifier.