1,255 research outputs found
Restrictive Voting Technique for Faces Spoofing Attack
Face anti-spoofing has become widely used due to the increasing use of biometric authentication systems that rely on facial recognition. It is a critical issue in biometric authentication systems that aim to prevent unauthorized access. In this paper, we propose a modified version of majority voting that ensembles the votes of six classifiers for multiple video chunks to improve the accuracy of face anti-spoofing. Our approach involves sampling sub-videos of 2 seconds each with a one-second overlap and classifying each sub-video using multiple classifiers. We then ensemble the classifications for each sub-video across all classifiers to decide the complete video classification. We focus on the False Acceptance Rate (FAR) metric to highlight the importance of preventing unauthorized access. We evaluated our method using the Replay Attack dataset and achieved a zero FAR. We also reported the Half Total Error Rate (HTER) and Equal Error Rate (EER) and gained a better result than most state-of-the-art methods. Our experimental results show that our proposed method significantly reduces the FAR, which is crucial for real-world face anti-spoofing applications
Attack Resilience and Recovery using Physical Challenge Response Authentication for Active Sensors Under Integrity Attacks
Embedded sensing systems are pervasively used in life- and security-critical
systems such as those found in airplanes, automobiles, and healthcare.
Traditional security mechanisms for these sensors focus on data encryption and
other post-processing techniques, but the sensors themselves often remain
vulnerable to attacks in the physical/analog domain. If an adversary
manipulates a physical/analog signal prior to digitization, no amount of
digital security mechanisms after the fact can help. Fortunately, nature
imposes fundamental constraints on how these analog signals can behave. This
work presents PyCRA, a physical challenge-response authentication scheme
designed to protect active sensing systems against physical attacks occurring
in the analog domain. PyCRA provides security for active sensors by continually
challenging the surrounding environment via random but deliberate physical
probes. By analyzing the responses to these probes, and by using the fact that
the adversary cannot change the underlying laws of physics, we provide an
authentication mechanism that not only detects malicious attacks but provides
resilience against them. We demonstrate the effectiveness of PyCRA through
several case studies using two sensing systems: (1) magnetic sensors like those
found wheel speed sensors in robotics and automotive, and (2) commercial RFID
tags used in many security-critical applications. Finally, we outline methods
and theoretical proofs for further enhancing the resilience of PyCRA to active
attacks by means of a confusion phase---a period of low signal to noise ratio
that makes it more difficult for an attacker to correctly identify and respond
to PyCRA's physical challenges. In doing so, we evaluate both the robustness
and the limitations of PyCRA, concluding by outlining practical considerations
as well as further applications for the proposed authentication mechanism.Comment: Shorter version appeared in ACM ACM Conference on Computer and
Communications (CCS) 201
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