Spoofing attack

Abstract: Spoofing attacks are one of the security traits that biometric recognition systems are proven to be vulnerable to. When spoofed, a biometric recognition system is bypassed by presenting a copy of the biometric evidence of a valid user. Among all biometric modalities, spoofing a face recognition system is particularly easy to perform: all that is needed is a simple photograph of the user. In this paper, we address the problem of detecting face spoofing attacks. In particular, we inspect the potential of texture features based on Local Binary Patterns (LBP) and their variations on three types of attacks: printed photographs, and photos and videos displayed on electronic screens of different sizes. For this purpose, we introduce REPLAY-ATTACK, a novel publicly available face spoofing database which contains all the mentioned types of attacks. We conclude that LBP, with ∼15% Half Total Error Rate, show moderate discriminability when confronted with a wide set of attack types.

Abstract: Defending against distributed denial-of-service attacks is one of the hardest security problems on the Internet today. One difficulty to thwart these attacks is to trace the source of the attacks because they often use incorrect, or spoofed IP source addresses to disguise the true origin. In this paper, we present two new schemes, the advanced marking scheme and the authenticated marking scheme, which allow the victim to trace-back the approximate origin of spoofed IP packets. Our techniques feature low network and router overhead, and support incremental deployment. In contrast to previous work, our techniques have significantly higher precision (lower false positive rate) and fewer computation overhead for the victim to reconstruct the attack paths under large scale distributed denial-of-service attacks. Furthermore the authenticated marking scheme provides efficient authentication of routers' markings such that even a compromised router cannot forge or tamper markings from other uncompromised routers.

Abstract: Securing communications requires the establishment of cryptographic keys, which is challenging in mobile scenarios where a key management infrastructure is not always present. In this paper, we present a protocol that allows two users to establish a common cryptographic key by exploiting special properties of the wireless channel: the underlying channel response between any two parties is unique and decorrelates rapidly in space. The established key can then be used to support security services (such as encryption) between two users. Our algorithm uses level-crossings and quantization to extract bits from correlated stochastic processes. The resulting protocol resists cryptanalysis by an eavesdropping adversary and a spoofing attack by an active adversary without requiring an authenticated channel, as is typically assumed in prior information-theoretic key establishment schemes. We evaluate our algorithm through theoretical and numerical studies, and provide validation through two complementary experimental studies. First, we use an 802.11 development platform with customized logic that extracts raw channel impulse response data from the preamble of a format-compliant 802.11a packet. We show that it is possible to practically achieve key establishment rates of ~ 1 bit/sec in a real, indoor wireless environment. To illustrate the generality of our method, we show that our approach is equally applicable to per-packet coarse signal strength measurements using off-the-shelf 802.11 hardware.

Abstract: Current face biometric systems are vulnerable to spoofing attacks. A spoofing attack occurs when a person tries to masquerade as someone else by falsifying data and thereby gaining illegitimate access. Inspired by image quality assessment, characterization of printing artifacts, and differences in light reflection, we propose to approach the problem of spoofing detection from texture analysis point of view. Indeed, face prints usually contain printing quality defects that can be well detected using texture features. Hence, we present a novel approach based on analyzing facial image textures for detecting whether there is a live person in front of the camera or a face print. The proposed approach analyzes the texture of the facial images using multi-scale local binary patterns (LBP). Compared to many previous works, our proposed approach is robust, computationally fast and does not require user-cooperation. In addition, the texture features that are used for spoofing detection can also be used for face recognition. This provides a unique feature space for coupling spoofing detection and face recognition. Extensive experimental analysis on a publicly available database showed excellent results compared to existing works.