Perceptual ad-blocking is a novel approach that uses visual cues to detect online advertisements. Compared to classical filter lists, perceptual ad-blocking is believed to be less prone to an arms race with web publishers and ad-networks. In this work we use techniques from adversarial machine learning to demonstrate that this may not be the case. We show that perceptual ad-blocking engenders a new arms race that likely disfavors ad-blockers. Unexpectedly, perceptual ad-blocking can also introduce new vulnerabilities that let an attacker bypass web security boundaries and mount DDoS attacks. We first analyze the design space of perceptual ad-blockers and present a unified architecture that incorporates prior academic and commercial work. We then explore a variety of attacks on the ad-blocker's full visual-detection pipeline, that enable publishers or ad-networks to evade or detect ad-blocking, and at times even abuse its high privilege level to bypass web security boundaries. Our attacks exploit the unreasonably strong threat model that perceptual ad-blockers must survive. Finally, we evaluate a concrete set of attacks on an ad-blocker's internal ad-classifier by instantiating adversarial examples for visual systems in a real web-security context. For six ad-detection techniques, we create perturbed ads, ad-disclosures, and native web content that misleads perceptual ad-blocking with 100% success rates. For example, we demonstrate how a malicious user can upload adversarial content (e.g., a perturbed image in a Facebook post) that fools the ad-blocker into removing other users' non-ad content.

Chinese authorities have begun deploying a new surveillance tool: 'gait recognition' software that uses people's body shapes and how they walk to identify them, even when their faces are hidden from cameras. Already used by police on the streets of Beijing and Shanghai, 'gait recognition' is part of a push across China to develop artificial-intelligence and data-driven surveillance. However, it's raised concerns among critics about how far the technology will go. China is deploying a new surveillance tool: 'gait recognition' software that uses people's body shapes and how they walk to identify them, even when their faces are hidden. Chinese startup Watrix's software extracts a person's silhouette from video and analyzes the silhouette's movement to create a model of the way the person walks.

BEIJING – Chinese authorities have begun deploying a new surveillance tool: "gait recognition" software that uses people's body shapes and how they walk to identify them, even when their faces are hidden from cameras. Already used by police on the streets of Beijing and Shanghai, "gait recognition" is part of a push across China to develop artificial-intelligence and data-driven surveillance that is raising concern about how far the technology will go. Huang Yongzhen, the CEO of Watrix, said that its system can identify people from up to 50 meters (165 feet) away, even with their back turned or their face covered. Such a capability can fill a gap in what is offered by facial recognition, which needs close-up, high-resolution images of a person's face in order to work. "You don't need people's cooperation for us to be able to recognize their identity," Huang said in an interview in his Beijing office.

Researchers at the State University of New York in Korea have recently explored new ways to detect both machine and human-created fake images of faces. In their paper, published in ACM Digital Library, the researchers used ensemble methods to detect images created by generative adversarial networks (GANs) and employed pre-processing techniques to improve the detection of images created by humans using Photoshop. Over the past few years, significant advancements in image processing and machine learning have enabled the generation of fake, yet highly realistic, images. However, these images could also be used to create fake identities, make fake news more convincing, bypass image detection algorithms, or fool image recognition tools. "Fake face images have been a topic of research for quite some time now, but studies have mainly focused on photos made by humans, using Photoshop tools," Shahroz Tariq, one of the researchers who carried out the study told Tech Xplore.

Back in September, Google promised to bring Lens to image search -- now, the feature is live in the US for English language queries. The object recognition techology can help you find out more about particular items within a photo you're looking at. If you want to try it out, do a Google search on mobile and go to the Images tab. Say, you want to look for a new sofa -- simply search for "sofas," go to Images and tap on one of the results. You'll find the new Lens icon underneath the photo next to the Share option, and tapping it will make dots appear on objects you can explore further.

Current Flash X-ray single-particle diffraction Imaging (FXI) experiments, which operate on modern X-ray Free Electron Lasers (XFELs), can record millions of interpretable diffraction patterns from individual biomolecules per day. Due to the stochastic nature of the XFELs, those patterns will to a varying degree include scatterings from contaminated samples. Also, the heterogeneity of the sample biomolecules is unavoidable and complicates data processing. Reducing the data volumes and selecting high-quality single-molecule patterns are therefore critical steps in the experimental set-up. In this paper, we present two supervised template-based learning methods for classifying FXI patterns. Our Eigen-Image and Log-Likelihood classifier can find the best-matched template for a single-molecule pattern within a few milliseconds. It is also straightforward to parallelize them so as to fully match the XFEL repetition rate, thereby enabling processing at site.

Compressive Sensing (CS) theory asserts that sparse signal reconstruction is possible from a small number of linear measurements. Although CS enables low-cost linear sampling, it requires non-linear and costly reconstruction. Recent literature works show that compressive image classification is possible in CS domain without reconstruction of the signal. In this work, we introduce a DCT base method that extracts binary discriminative features directly from CS measurements. These CS measurements can be obtained by using (i) a random or a pseudo-random measurement matrix, or (ii) a measurement matrix whose elements are learned from the training data to optimize the given classification task. We further introduce feature fusion by concatenating Bag of Words (BoW) representation of our binary features with one of the two state-of-the-art CNN-based feature vectors. We show that our fused feature outperforms the state-of-the-art in both cases.

We learn such a generative model for each digit. Then, when a new input comes along, we check which digit model can best approximate the new input. This procedure is typically called analysis-by-synthesis, because we analyse the content of the image according to the model that can best synthesise it. That's really the key difference: feedforward networks have no way to check their predictions, you have to trust them. Our analysis-by-synthesis model, on the other hand, looks whether certain image features are really present in the input before jumping to a conclusion.

There's nothing worse than having to pore over a pile of PDFs containing documents scanned as images when you quickly have to find a specific file. Dropbox is making it easier to do that by introducing automatic image recognition, which extracts texts from photos and PDFs and makes them searchable. According to the cloud storage provider, there are 20 billion image and PDF files stored on Dropbox. Around 10 to 20 percent of those are photos of documents, so the new feature can be very, very useful. To look for a specific photo or PDF, you simply have to type in a keyword or phrase like you would on a search engine.

Authorities say the Canyon County jail is the first in Idaho to implement an iris biometric identification recognition system when booking inmates into custody, a process that is more accurate and faster than fingerprinting.