Since the COVID-19 pandemic, the rise of video con-ferencing has introduced new information security challenges, particularly the unintentional leakage of sensitive information via reflected text on eyeglass surfaces captured by webcams. While prior studies have shown the possibility of webcam peeking attacks, they typically rely on manual analysis or costly equipment, limiting scalability. To address these challenges, we propose Optical Character Recognition (OCR) of Reflected text, called ORT, an automated attack model that extracts re-flected text using advanced OCR techniques. ORT is composed of customized preprocessing steps and optimized Scene Text Detection (STD) and Scene Text Recognition (STR) models, with the STD model retrained on a custom reflected text dataset to enhance performance under conditions such as blur, low contrast, and distortion. We evaluated ORT in various video conferencing environments, taking into account variables such as lighting, text size, background color, and the presence or absence of prescriptions for eyeglasses. The results show high recognition accuracy, demonstrating that ORT significantly reduces adversary effort and raises the threat level of webcam peeking attacks.

Radio-frequency identification (RFID) system, successfully adopted in many industrial applications, suffers from security issues due to the inherent weakness of wireless communication, such as eavesdropping, replay attack, impersonation attack, and traceability issues. A lot of research efforts based on cryptographic primitives have been conducted in a decade, however, most of the existing security protocols depending on cryptosystems are not feasible to be applied due to the minimalist design of passive tags. A lightweight cryptographic authentication is one of the practical solutions, but it has traceability issues from physical layer information. The other approach is to use the properties of the physical layer of RFID systems. However, since the physical-layer characteristics cannot be intentionally updated, they are vulnerable to situations where an adversary actively obtains authentic data for traceability attacks or replay attacks. Therefore, to resist the security threats, we propose Tagora, a cross-layer authentication protocol, which is the first integration work of two-layer approaches that harness the unpredictable properties of tag's collision responses at both the physical and application layers. Our protocol design is composed of a collision recovery algorithm with a random offset scheme and phase encryption in the physical layer, and authentication process based on a challenge-response mechanism in the application layer. We evaluate Tagora in terms of the untraceability and reliability, and also provide security analysis on how Tagora can defend against plausible attacks while meeting security requirements.

The prosperity of IEEE 802.11-based Wi-Fi networks aggravates cross-technology interference to IEEE 802.15.4-enabled ZigBee networks widely deployed to enable various Internet-of-Things applications. To make ZigBee communication reliable and robust even in a dense Wi-Fi environment, taming Wi-Fi interference in ZigBee networks especially from the perspective of physical layer is of paramount importance. In this context, this work takes aim to design a novel Wi-Fi interference-resilient ZigBee decoder called PolarScout, which separates collided ZigBee signal samples out of Wi-Fi interference to bootstrap ZigBee data decoding. Unlike several existing solutions which need clear signal preamble, tremendous signal strength difference between ZigBee and Wi-Fi, and Wi-Fi interference recognition in prior to ZigBee decoding, PolarScout aims at direct ZigBee decoding in a more generic and challenging case where Wi-Fi interference features a wide range of power levels and arises within a ZigBee packet at an arbitrary position. At the heart of PolarScout lies a subtle shell-shaping technique which harnesses a customized sample sequence to smooth the shell of corrupted signal samples. PolarScout then refers to the resulting shell to recover each contaminated ZigBee sample. Experimental results validate the superiority of PolarScout and its resilience to a wide range of Wi-Fi interference types.