• Journal of Positioning, Navigation, and Timing
• /
• v.7 no.3
• /
• pp.127-138
• /
• 2018

Spoofing attacks including meaconing can provide a bogus position to a victim GPS receiver, and those attacks are notably difficult to detect at the point of view on the receiver. Several countermeasure techniques have been studied to detect, classify, and cancel the spoofing signals. Based on the countermeasure techniques, we have developed an anti-spoofing equipment that detects and mitigates or eliminates the spoofing signal based on raw measurements. Although many anti-spoofing techniques have been studied in the literatures, the evaluation test system is not deeply studied to evaluate the anti-spoofing equipment, which includes detection, mitigation, and elimination of spoofing signals. Each study only has a specific test method to verify its anti-spoofing technique. In this paper, we propose the performance evaluation test system that includes both spoofing signal injection system and its injection scenario with the constraints of stand-alone anti-spoofing techniques. The spoofing signal injection scenario is designed to drive a victim GPS receiver that moves to a designed position, where the mitigation and elimination based anti-spoofing algorithms can be successively evaluated. We evaluate the developed anti-spoofing equipment and a commercial GPS receiver using our proposed performance evaluation test system. Although the commercial one is affected by the test system and moves to the designed position, the anti-spoofing equipment mitigates and eliminates the injected spoofing signals as planned. We evaluate the performance of anti-spoofing equipment on the position error of the circular error probability, while injecting spoofing signals.

• The Journal of the Korea institute of electronic communication sciences
• /
• v.19 no.1
• /
• pp.309-316
• /
• 2024

Facial recognition technology is widely used in various fields but faces challenges due to its vulnerability to fraudulent activities such as photo spoofing. Extensive research has been conducted to overcome this challenge. Most of them, however, require the use of specialized equipment like multi-modal cameras or operation in high-performance environments. In this paper, we introduce LH-FAS v2 (: Lightweight Head-pose-based Face Anti-Spoofing v2), a system designed to operate on a commercial webcam without any specialized equipment, to address the issue of facial recognition spoofing. LH-FAS v2 utilizes FSA-Net for head pose estimation and ArcFace for facial recognition, effectively assessing changes in head pose and verifying facial identity. We developed the VD4PS dataset, incorporating photo spoofing scenarios to evaluate the model's performance. The experimental results show the model's balanced accuracy and speed, indicating that head pose estimation-based facial anti-spoofing technology can be effectively used to counteract photo spoofing.

• Journal of Communications and Networks
• /
• v.11 no.6
• /
• pp.544-555
• /
• 2009

This work continues a trend of developments aimed at exploiting the physical layer of the open systems interconnection (OSI) model to enhance wireless network security. The goal is to augment activity occurring across other OSI layers and provide improved safeguards against unauthorized access. Relative to intrusion detection and anti-spoofing, this paper provides details for a proof-of-concept investigation involving "air monitor" applications where physical equipment constraints are not overly restrictive. In this case, RF fingerprinting is emerging as a viable security measure for providing device-specific identification (manufacturer, model, and/or serial number). RF fingerprint features can be extracted from various regions of collected bursts, the detection of which has been extensively researched. Given reliable burst detection, the near-term challenge is to find robust fingerprint features to improve device distinguishability. This is addressed here using wavelet domain (WD) RF fingerprinting based on dual-tree complex wavelet transform (DT-$\mathbb{C}WT$) features extracted from the non-transient preamble response of OFDM-based 802.11a signals. Intra-manufacturer classification performance is evaluated using four like-model Cisco devices with dissimilar serial numbers. WD fingerprinting effectiveness is demonstrated using Fisher-based multiple discriminant analysis (MDA) with maximum likelihood (ML) classification. The effects of varying channel SNR, burst detection error and dissimilar SNRs for MDA/ML training and classification are considered. Relative to time domain (TD) RF fingerprinting, WD fingerprinting with DT-$\mathbb{C}WT$ features emerged as the superior alternative for all scenarios at SNRs below 20 dB while achieving performance gains of up to 8 dB at 80% classification accuracy.