In recent years, Generative Adversarial Networks (GANs) have become essential tools in artificial intelligence research. Field Programmable Gate Arrays (FPGAs) offer remarkable flexibility, high performance, and energy efficiency for deploying GANs. However, the open and reprogrammable architecture of FPGAs, despite its advantages, introduces risks of unauthorized access and reverse engineering. To address this challenge, this paper presents a novel approach integrating Physical Unclonable Functions (PUFs) and logos to protect the Intellectual Property Rights (IPR) of GANs. Our method establishes a closed-loop conversion process where logos are transformed into PUF responses, generating unique identities fed into the GAN to reproduce the original logo. By embedding PUF response information into latent vectors, the generator produces images with embedded logos. Thanks to the uniqueness of PUF, a robust binding of the logo, FPGA, and GANs' IPR is implemented, allowing verification of the IPR with the assistance of a unique FPGA fingerprint, even when a publicly available logo is used. Experimental results show that embedding the logo does not change the performance of the original GANs, and the logo detection rate exceeds 90%. At the same time, the scheme can effectively resist brute force, fine-tuning and pruning attacks.

The space-air-ground integrated network (SAGIN) has a stringent demand on the efficiency of authentication protocols deployed in the devices that have been launched into the air and space. In this paper, we define the concept of the security model of conditional physical unclonable function (CPUF) that guarantees the security of the protocol while allowing the use of PUFs that can be modeled. We then propose a CPUF-based authentication and key agreement (AKA) scheme, named CPAKA, that addresses the challenges of device key leakage and inefficient authentication in resource-asymmetric environments. The CPAKA scheme embeds PUFs in weak nodes and deploys prediction models corresponding to the PUFs in strong nodes, eliminating the need to store challenge-response pairs or perform complex calculations. We formally prove the protocol's security under the decisional uniqueness assumption of CPUF and the universal composability framework, and we analyze its secrecy and authentication properties using the Tamarin prover. We also implement an Arbiter PUF on the ZYNQ-7020 FPGA, verify its accuracy through experiments, and show that CPAKA is secure, efficient, and suitable for SAGIN. Our CPAKA scheme greatly reduces computing and storage costs while improving authentication efficiency compared to traditional schemes.

The device fingerprinting technique extracts fingerprints based on the hardware characteristics of the device to identify the device. The primary goal of device fingerprinting is to accurately and uniquely identify a device, which requires the generated device fingerprints to have good stability to achieve long-term tracking of the target device. However, the fingerprints generated by some existing fingerprinting technologies are not stable enough or change frequently, making it impossible to track the target device for a long time. In this paper, we present FPHammer, a novel DRAM-based fingerprinting technique. The device fingerprint generated by our technique has high stability and can be used to track the device for a long time. We leverage the Rowhammer technique to repeatedly and quickly access a row in DRAM to get bit flips in its adjacent row. We then construct a physical fingerprint of the device based on the locations of the collected bit flips. The evaluation results of the uniqueness and reliability of the physical fingerprint show that it can be used to distinguish devices with the same hardware and software configuration. The experimental results on device identification demonstrate that the physical fingerprints engendered by our innovative technique are inherently linked to the entirety of the device rather than just the DRAM module. Even if the device modifies software-level parameters such as MAC address and IP address or even reinstalls the operating system, we can accurately identify the target device. This demonstrates that FPHammer can generate stable fingerprints that are not affected by software layer parameters.