GanFinger: GAN-Based Fingerprint Generation for Deep Neural Network Ownership Verification
This addresses the issue of unauthorized use of DNNs for owners, but it is incremental as it builds on existing network fingerprinting approaches.
The paper tackles the problem of verifying ownership of deep neural networks (DNNs) by proposing GanFinger, a GAN-based fingerprint generation method that uses network outputs from original and adversarial examples. The result shows it is 6.57 times faster in fingerprint generation and improves the ARUC value by 0.175, a 26% relative gain compared to state-of-the-art methods.
Deep neural networks (DNNs) are extensively employed in a wide range of application scenarios. Generally, training a commercially viable neural network requires significant amounts of data and computing resources, and it is easy for unauthorized users to use the networks illegally. Therefore, network ownership verification has become one of the most crucial steps in safeguarding digital assets. To verify the ownership of networks, the existing network fingerprinting approaches perform poorly in the aspects of efficiency, stealthiness, and discriminability. To address these issues, we propose a network fingerprinting approach, named as GanFinger, to construct the network fingerprints based on the network behavior, which is characterized by network outputs of pairs of original examples and conferrable adversarial examples. Specifically, GanFinger leverages Generative Adversarial Networks (GANs) to effectively generate conferrable adversarial examples with imperceptible perturbations. These examples can exhibit identical outputs on copyrighted and pirated networks while producing different results on irrelevant networks. Moreover, to enhance the accuracy of fingerprint ownership verification, the network similarity is computed based on the accuracy-robustness distance of fingerprint examples'outputs. To evaluate the performance of GanFinger, we construct a comprehensive benchmark consisting of 186 networks with five network structures and four popular network post-processing techniques. The benchmark experiments demonstrate that GanFinger significantly outperforms the state-of-the-arts in efficiency, stealthiness, and discriminability. It achieves a remarkable 6.57 times faster in fingerprint generation and boosts the ARUC value by 0.175, resulting in a relative improvement of about 26%.