EfficientWord-Net: An Open Source Hotword Detection Engine based on One-shot Learning

Voice assistants like Siri, Google Assistant, Alexa etc. are used widely across the globe for home automation, these require the use of special phrases also known as hotwords to wake it up and perform an action like "Hey Alexa!", "Ok Google!" and "Hey Siri!" etc. These hotwords are detected with lightweight real-time engines whose purpose is to detect the hotwords uttered by the user. This paper presents the design and implementation of a hotword detection engine based on one-shot learning which detects the hotword uttered by the user in real-time with just one or few training samples of the hotword. This approach is efficient when compared to existing implementations because the process of adding a new hotword in the existing systems requires enormous amounts of positive and negative training samples and the model needs to retrain for every hotword. This makes the existing implementations inefficient in terms of computation and cost. The architecture proposed in this paper has achieved an accuracy of 94.51%.Comment: 9 pages, 17 figure

Joint Neural Networks for One-shot Object Recognition and Detection.

PhD ThesesThis thesis presents a study of techniques for one-shot recognition and detec- tion of objects. In computer vision, the one-shot object recognition task aims to categorize matches and mismatches between patterns from which a single sample is available for training. Whereas, the one-shot object detection task additionally de nes a bounding box locating the position of the matching pat- tern within a target image. Such task is trivial for humans but challenging for machines. Classical object recognition and detection have drawn a considerable amount of attention in the last decades leading to the development of several machine learning related approaches. Most approaches require a substantial amount of data to achieve state-of-art performance; making them unsuitable for use cases where collecting the data can be more costly than performing the task manu- ally. In contrast, a relatively low amount of work is present in the literature tackling the one-shot recognition problem. Just until recently, Siamese Neural Networks have emerged in the literature as a deep learning approach to ad- dress the one-shot object recognition and detection problems while achieving reasonable results This research focuses on building modi cations to pairwise Siamese Neural Networks introducing the concept of joint layer, that improve their performance when addressing the one-shot object recognition problem in realistic scenarios. The proposed Joint Neural Networks achieved an accuracy of 70.0% when com- pared with the standard Siamese Neural Networks approach, outperforming the later by 10.0% accuracy tested on the MiniImageNet and QMUL-OpenLogo datasets. Subsequently, a novel approach for one-shot object detection is presented as an extension of the Joint Neural Networks. The proposed one-shot detection approach is inspired by state-of-art one-stage detection approaches and does not rely on contextual information or support test sets to generate predictions. This approach shows competitive results, achieving a 56.1% mAP on the Pascal VOC dataset split for one-shot detection. In adition, Joint Neural Networks push the state-of-art when trained on the COCO dataset and tested on the pascal VOC dataset, achieving 47.1% mAP.

VisualPhishNet: Zero-Day Phishing Website Detection by Visual Similarity

Phishing websites are still a major threat in today's Internet ecosystem. Despite numerous previous efforts, similarity-based detection methods do not offer sufficient protection for the trusted websites - in particular against unseen phishing pages. This paper contributes VisualPhishNet, a new similarity-based phishing detection framework, based on a triplet Convolutional Neural Network (CNN). VisualPhishNet learns profiles for websites in order to detect phishing websites by a similarity metric that can generalize to pages with new visual appearances. We furthermore present VisualPhish, the largest dataset to date that facilitates visual phishing detection in an ecologically valid manner. We show that our method outperforms previous visual similarity phishing detection approaches by a large margin while being robust against a range of evasion attacks