ArmSSL: Adversarial Robust Black-Box Watermarking for Self-Supervised Learning Pre-trained Encoders
In recent developments within the domain of artificial intelligence, the protection of intellectual property (IP) associated with self-supervised learning (SSL) encoders has emerged as a critical concern. Researchers have recognized that while SSL encoders are invaluable assets, existing watermarking techniques fall short in meeting essential requirements for ownership verification and robustness against adversarial attacks. The newly proposed framework, ArmSSL, addresses these challenges by providing a comprehensive solution for watermarking SSL encoders.
Challenges in Existing SSL Watermarking Techniques
The current landscape of watermarking techniques for SSL encoders faces two primary challenges:
- Ownership Verification: Most existing methods do not allow for ownership verification under a black-box suspect model. This limitation poses a significant risk, as stolen encoders may be utilized in downstream tasks without a reliable means for the original owners to assert their rights.
- Adversarial Robustness: Watermarks often form distinguishable out-of-distribution (OOD) clusters, making them vulnerable to adversarial detection or removal. This vulnerability undermines the effectiveness of watermarking as a protective measure.
Introducing ArmSSL Framework
In response to these challenges, the ArmSSL framework has been introduced, which incorporates innovative strategies to enhance both verification capabilities and adversarial robustness while maintaining the utility of SSL encoders. Key features of ArmSSL include:
- Paired Discrepancy Enlargement: This approach enforces feature-space orthogonality between the clean encoder and its watermarked counterpart. By producing a reliable verification signal, ArmSSL ensures ownership verification can occur even under black-box conditions.
- Latent Representation Entanglement: To combat adversarial robustness, ArmSSL entangles watermark representations with clean representations from non-source classes. This technique prevents the formation of dense clusters of watermark samples that are easily distinguishable.
- Distribution Alignment: ArmSSL minimizes the distributional discrepancy between watermark and clean representations. This integration helps to disguise watermark samples as natural in-distribution data, thereby enhancing robustness against adversarial attacks.
- Reference-Guided Watermark Tuning Strategy: This unique strategy allows the watermark to be learned as a small side task, which aligns the outputs of the watermarked encoder with those of the original clean encoder on normal data. This ensures that the main task is not adversely affected, preserving the utility of the encoder.
Experimental Validation
Extensive experiments conducted across five mainstream SSL frameworks and nine benchmark datasets have demonstrated the efficacy of the ArmSSL framework. The results indicate that ArmSSL achieves superior ownership verification capabilities, negligible degradation in utility, and strong robustness against various adversarial detection and removal techniques.
Conclusion
As the field of self-supervised learning continues to advance, the need for robust IP protection mechanisms becomes increasingly vital. ArmSSL represents a significant step forward in addressing the challenges associated with watermarking SSL encoders. By ensuring black-box verifiability and adversarial robustness while maintaining the encoder’s utility, ArmSSL not only enhances the security of SSL models but also contributes to the broader landscape of AI and machine learning integrity.
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