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Stdatav2: Accessing Efficient Black-box Stealing For Adversarial Attacks.

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Stdatav2: Accessing Efficient Black-box Stealing For Adversarial Attacks.

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View abstract on PubMed

Summary

This summary is machine-generated.

This study enhances surrogate model training by using both synthesized and proxy data to prevent mode collapse. A new self-conditional framework ensures data diversity and class-specific constraints for improved black-box model stealing defenses.

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Area of Science:

Machine Learning
Artificial Intelligence
Cybersecurity

Background:

Stealing black-box models without training data is challenging due to extreme settings.
Existing methods like Surrogate Training Data (STDatav1) face limitations such as potential mode collapse and maintaining data diversity.

Purpose of the Study:

To improve the effectiveness of surrogate model training for black-box model stealing.
To mitigate mode collapse and enhance data diversity and class-specific constraints in surrogate data generation.

Main Methods:

Proposed a joint-data optimization scheme using both synthesized and proxy data to train the surrogate model.
Introduced a self-conditional data synthesis framework with pseudo-class mapping for class-specific constraints and diversity.

Integrated STDatav1's class-specific constraints and designed a dual cross-entropy loss function.

Main Results:

Demonstrated considerable performance gains compared to the previous STDatav1.
Evaluations on four datasets using eight models confirmed the approach's competitive ability and potential.
The proposed methods effectively addressed mode collapse and maintained data diversity and class-specific constraints.

Conclusions:

The enhanced surrogate training approach offers significant improvements for black-box model stealing.
The joint-data optimization and self-conditional synthesis framework represent a promising advancement in the field.
This work provides a robust method for generating high-quality surrogate data, advancing research in model privacy and security.