AdaptivePixGuard: Attention-Enhanced Temporal Convolutions for Robust Mobile Pixnapping Detection
Siwar Rekik, Sajid Mehmood, Mimouna Abdullah Alkhonaini · IEEE Access · 2026
Pixnapping attacks rely on Android vulnerabilities in the UI rendering pipeline that allows sensitive on-screen pixel data such as OTPs or passwords to be stolen without any screenshot permissions being requested by the attacker. The attacks circumvent conventional security systems, which are very dangerous to mobile users. In this paper, This paper presents AdaptivePixGuard, a framework of real-time pixnapping attack detection and mitigation based on a lightweight yet adaptable AI framework. The framework uses a Temporal Convolutional Network (TCN) with self-attention to take dynamic behavioral sequences such as system calls, overlay events, and pixel entropy changes. AdaptivePixGuard integrates online gradual learning to ensure a desirable detection accuracy in changing threat settings. This Work is tested on CIC-AndMal2017 and CICMalDroid 2020 datasets showing 96.2% for categorization accuracy, AUC of 0.98% and CPU overhead of less than 4% on mid-range Android devices with mitigation executed in 180 ms. These findings demonstrate the usefulness of the framework in real-world practice to offer a valuable countermechanism to zero-permission pixel-exfiltration attacks.