IEEE TIFS Article

Electric network frequency (ENF) is a time-varying signal of the frequency of mains electricity in a power grid. It continuously fluctuates around a nominal value (50/60 Hz) due to changes in the supply and demand of power over time. Depending on these ENF variations, the luminous intensity of a mains-powered light source also fluctuates. 

Face presentation attacks are the main threats to face recognition systems, and many presentation attack detection (PAD) methods have been proposed in recent years. Although these methods have achieved significant performance in some specific intrusion modes, difficulties still exist in addressing replayed video attacks. 

Low-level criminals, who do the legwork in a criminal organization, are the most likely to be arrested, whereas the high-level ones tend to avoid attention. But crippling the work of criminal organizations is not possible unless investigators can identify the most influential, high-level members and monitor their communication channels.

Current anomaly detection systems (ADSs) apply statistical and machine learning algorithms to discover zero-day attacks, but such algorithms are vulnerable to advanced persistent threat actors. In this paper, we propose an adversarial statistical learning mechanism for anomaly detection, outlier Dirichlet mixture-based ADS (ODM-ADS), which has three new capabilities.

Steganographic schemes are commonly designed in a way to preserve image statistics or steganalytic features. Since most of the state-of-the-art steganalytic methods employ a machine learning (ML)-based classifier, it is reasonable to consider countering steganalysis by trying to fool the ML classifiers.

Automated biometric identification systems are inherently challenged to optimize false (non-)match rates. This can be addressed either by directly improving comparison subsystems, or indirectly by allowing only “good quality” biometric queries to be compared.

The task of Heterogeneous Face Recognition consists in matching face images that are sensed in different domains, such as sketches to photographs (visual spectra images), and thermal images to photographs or near-infrared images to photographs. In this paper, we suggest that the high-level features of Deep Convolutional Neural Networks trained in visual spectra images are potentially domain independent and can be used to encode faces sensed in different image domains.

In psychology, it is known that facial dynamics benefit the perception of identity. This paper proposes a novel deep network framework to capture identity information from facial dynamics and their relations. In the proposed method, facial dynamics occurred from a smile expression are analyzed and utilized for facial authentication. Detailed changes in the local regions of a face such as wrinkles and dimples are encoded in the facial dynamic feature representation. 

In real-world applications, different kinds of learning and prediction errors are likely to incur different costs for the same system. Moreover, in practice, the cost label information is often available only for a few training samples. In a semi-supervised setting, label propagation is critical to infer the cost information for unlabeled training data.

Auction is an effective way to allocate goods or services to bidders who value them the most. The rapid growth of e-auctions facilitates online transactions but poses new and distinctive challenges. It is difficult to establish trust among sellers, buyers, and auctioneers without centralized auction websites or platforms (the auctioneer) which collect bids and derive the auction results. However, these third parties may be untrustworthy, and malicious sellers or buyers may refuse to deliver the goods or payment according to the protocol.