Latent Regularized Generative Dual Adversarial Network For Abnormal Detection

With the development of adversarial attack in deep learning, it is critical for abnormal detector to not only discover the out-of-distribution samples but also provide defence against the adversarial attacker. Since few previous universal detector is known to work well on both tasks, we consider against both scenarios by constructing a robust and effective technique, where one sample could be regarded as the abnormal sample if it exhibits a higher image reconstruction error. Due to the training instability issues existed in previous generative adversarial networks (GANs) based methods, in this paper we propose a dual auxiliary autoencoder to make a tradeoff between the capability of generator and discriminator, leading to a more stable training process and high-quality image reconstruction. Moreover, to generate discriminative and robust latent representations, the mutual information estimator regarded as latent regularizer is adopted to extract the most unique information of target class. Overall, our generative dual adversarial network simultaneously optimizes the image reconstruction space and latent space to improve the performance. Experiments show that our model has the clear superiority over cutting edge semi-supervised abnormal detectors and achieves the state-of-the-art results on the datasets.

Score-based likelihood ratios for camera device identification

Many areas of forensics are moving away from the notion of classifying evidence simply as a match or non-match. Instead, some use score-based likelihood ratios (SLR) to quantify the similarity between two pieces of evidence, such as a fingerprint obtained from a crime scene and a fingerprint obtained from a suspect. We apply trace-anchored score-based likelihood ratios to the camera device identification problem. We use photo-response non-uniformity (PRNU) as a camera fingerprint and one minus the normalized correlation as a similarity score. We calculate trace-anchored SLRs for 10,000 images from seven camera devices from the BOSSbase image dataset. We include a comparison between our results the universal detector method.

Eddy-Current Sensor for a Tachometer

The speed of a rotor or rotors of a turbine engine is important information on its operation. Due to the specific conditions, in which the measurement is taken in a turbine engine, which in particular applies to small engines that are becoming increasingly popular in manned and unmanned aviation, the most universal detector is the detector using the phenomenon of eddy currents. This article presents the developed detector system solution. The correct operation of a detector is conditional upon the possibility to detect the presence of a blade in the detection field in a reliable manner, while maintaining the resistance to interference. The developed system is resistant to changes in parameters due to the use of a differential system consisting of two detector coils connected in a bridge circuit, a rectifier circuit with filters acting as a demodulator and a comparator. The system works with a microprocessor performing data processing functions. The executed functional model was tested in a laboratory, which enabled to determine its characteristics in several configurations and to confirm its correct operation.