Invisible Pixels Are Dead, Long Live Invisible Pixels!

Privacy has deteriorated in the world wide web ever since the 1990s. The tracking of browsing habits by different third-parties has been at the center of this deterioration. Web cookies and so-called web beacons have been the classical ways to implement third-party tracking. Due to the introduction of more sophisticated technical tracking solutions and other fundamental transformations, the use of classical image-based web beacons might be expected to have lost their appeal. According to a sample of over thirty thousand images collected from popular websites, this paper shows that such an assumption is a fallacy: classical 1 x 1 images are still commonly used for third-party tracking in the contemporary world wide web. While it seems that ad-blockers are unable to fully block these classical image-based tracking beacons, the paper further demonstrates that even limited information can be used to accurately classify the third-party 1 x 1 images from other images. An average classification accuracy of 0.956 is reached in the empirical experiment. With these results the paper contributes to the ongoing attempts to better understand the lack of privacy in the world wide web, and the means by which the situation might be eventually improved.Comment: Forthcoming in the 17th Workshop on Privacy in the Electronic Society (WPES 2018), Toronto, AC

Revising Knowledge Discovery for Object Representation with Spatio-Semantic Feature Integration

In large social networks, web objects become increasingly popular. Multimedia object classification and representation is a necessary step of multimedia information retrieval. Indexing and organizing these web objects for the purpose of convenient browsing and search of the objects, and to effectively reveal interesting patterns from the objects. For all these tasks, classifying the web objects into manipulable semantic categories is an essential procedure. One important issue for classification of objects is the representation of images. To perform supervised classification tasks, the knowledge is extracted from unlabeled objects through unsupervised learning. In order to represent the images in a more meaningful and effective way rather than using the basic Bag-of-words (BoW) model, a novel image representation model called Bag-of-visual phrases(BoP) is used. In this model visual words are obtained using hierarchical clustering and visual phrases are generated by vector classifier of visual words. To obtain the Spatio-semantic correlation knowledge the frequently co-occurring pairs are calculated from visual vocabulary. After the successful object representation, the tags, comments, and descriptions of web objects are separated by using most likelihood method. The spatial and semantic differentiation power of image features can be enhanced via this BoP model and likelihood method. DOI: 10.17762/ijritcc2321-8169.15065

Semantic Based Answering Technique for Image Query in Mobile Cloud Computing

This paper aims an answering technique that identifies the disease name in tomato plants by giving the affected plant�s image as input and enables the users to retrieve the preventive and controlling methods of the disease. Classifying an image accurately, takes different forms in different researches. Content Based Image Retrieval and Google�s reverse image search are few outcomes of such researches. Still, there is a need for a technique that recognizes images like how humans classify based on their experience. This work comes with a better solution by combining image classification in human�s perspective with semantic based answering. TensorFlow is an open source algorithm that is released by Google is an effective tool for classifying images and ontology that gives very accurate answers to the user queries are the technologies that are used in the proposed technique. The images and details of tomato crop diseases are collected from different forums and the glossary terms used in ontology are taken from the web

Feature Representation Analysis of Deep Convolutional Neural Network using Two-stage Feature Transfer -An Application for Diffuse Lung Disease Classification-

Transfer learning is a machine learning technique designed to improve generalization performance by using pre-trained parameters obtained from other learning tasks. For image recognition tasks, many previous studies have reported that, when transfer learning is applied to deep neural networks, performance improves, despite having limited training data. This paper proposes a two-stage feature transfer learning method focusing on the recognition of textural medical images. During the proposed method, a model is successively trained with massive amounts of natural images, some textural images, and the target images. We applied this method to the classification task of textural X-ray computed tomography images of diffuse lung diseases. In our experiment, the two-stage feature transfer achieves the best performance compared to a from-scratch learning and a conventional single-stage feature transfer. We also investigated the robustness of the target dataset, based on size. Two-stage feature transfer shows better robustness than the other two learning methods. Moreover, we analyzed the feature representations obtained from DLDs imagery inputs for each feature transfer models using a visualization method. We showed that the two-stage feature transfer obtains both edge and textural features of DLDs, which does not occur in conventional single-stage feature transfer models.Comment: Preprint of the journal article to be published in IPSJ TOM-51. Notice for the use of this material The copyright of this material is retained by the Information Processing Society of Japan (IPSJ). This material is published on this web site with the agreement of the author (s) and the IPS

Learning Deep Visual Object Models From Noisy Web Data: How to Make it Work

Deep networks thrive when trained on large scale data collections. This has given ImageNet a central role in the development of deep architectures for visual object classification. However, ImageNet was created during a specific period in time, and as such it is prone to aging, as well as dataset bias issues. Moving beyond fixed training datasets will lead to more robust visual systems, especially when deployed on robots in new environments which must train on the objects they encounter there. To make this possible, it is important to break free from the need for manual annotators. Recent work has begun to investigate how to use the massive amount of images available on the Web in place of manual image annotations. We contribute to this research thread with two findings: (1) a study correlating a given level of noisily labels to the expected drop in accuracy, for two deep architectures, on two different types of noise, that clearly identifies GoogLeNet as a suitable architecture for learning from Web data; (2) a recipe for the creation of Web datasets with minimal noise and maximum visual variability, based on a visual and natural language processing concept expansion strategy. By combining these two results, we obtain a method for learning powerful deep object models automatically from the Web. We confirm the effectiveness of our approach through object categorization experiments using our Web-derived version of ImageNet on a popular robot vision benchmark database, and on a lifelong object discovery task on a mobile robot.Comment: 8 pages, 7 figures, 3 table