Need more data for deep learning? Synthetic data companies will make it for you.
You can see the faint stubble coming in on his upper lip, the wrinkles on his forehead, the blemishes on his skin. He isn’t a real person, but he’s meant to mimic one—as are the hundreds of thousands of others made by Datagen, a company that sells fake, simulated humans.
These humans are not gaming avatars or animated characters for movies. They are synthetic data designed to feed the growing appetite of deep-learning algorithms. Firms like Datagen offer a compelling alternative to the expensive and time-consuming process of gathering real-world data. They will make it for you: how you want it, when you want—and relatively cheaply. Read More
Monthly Archives: June 2021
A Survey of Deep Learning Methods for Cyber Security
This survey paper describes a literature review of deep learning (DL) methods for cybersecurity applications. A short tutorial-style description of each DL method is provided, including deep autoencoders, restricted Boltzmann machines, recurrent neural networks, generative adversarial networks, and several others. Then we discuss how each of the DL methods is used for security applications. We cover a broad array of attack types including malware, spam, insider threats, network intrusions, false data injection, and malicious domain names used by botnets. Read More
Review: Deep Learning techniques for Cyber Security
Cybersecurity researchers build a better ‘canary trap’
A new artificial intelligence system generates fake docs to fool adversaries
The “canary trap” technique in espionage spreads multiple versions of false documents to conceal a secret. Canary traps can be used to sniff out information leaks, or as in WWII, to create distractions that hide valuable information.
WE-FORGE, a new data protection system designed at Dartmouth’s Department of Computer Science, uses artificial intelligence to build on the canary trap concept. The system automatically creates false documents to protect intellectual property such as drug design and military technology. Read More
The Modern Mathematics of Deep Learning
We describe the new field of mathematical analysis of deep learning. This field emerged around a list
of research questions that were not answered within the classical framework of learning theory. These
questions concern: the outstanding generalization power of overparametrized neural networks, the role of
depth in deep architectures, the apparent absence of the curse of dimensionality, the surprisingly successful
optimization performance despite the non-convexity of the problem, understanding what features are
learned, why deep architectures perform exceptionally well in physical problems, and which fine aspects
of an architecture affect the behavior of a learning task in which way. We present an overview of modern
approaches that yield partial answers to these questions. For selected approaches, we describe the main
ideas in more detail. Read More
GPT-3 Scared You? Meet Wu Dao 2.0: A Monster of 1.75 Trillion Parameters
We’re living exciting times in AI. OpenAI shocked the world a year ago with GPT-3. Two weeks ago Google presented LaMDA and MUM, two AIs that will revolutionize chatbots and the search engine, respectively. And just a few days ago, on the 1st of June, the Beijing Academy of Artificial Intelligence (BAAI) conference presented Wu Dao 2.0.
Wu Dao 2.0 is now the largest neural network ever created and probably the most powerful. Its potential and limits are yet to be fully disclosed, but the expectations are high and rightly so.
In this article, I’ll review the available information about Wu Dao 2.0: What it is, what it can do, and what are the promises of its creators for the future. Enjoy! Read More
TextStyleBrush: Transfer of text aesthetics from a single example
We present a novel approach for disentangling the content of a text image from all aspects of its appearance. The appearance representation we derive can then be applied to new content, for one-shot transfer of the source style to new content. We learn this disentanglement in a self-supervised manner. Our method processes entire word boxes, without requiring segmentation of text from background, per-character processing, or making assumptions on string lengths. We show results in different text domains which were previously handled by specialized methods, e.g., scene text, handwritten text. To these ends, we make a number of technical contributions: (1) We disentangle the style and content of a textual image into a non-parametric, fixed-dimensional vector. (2) We propose a novel approach inspired by StyleGAN but conditioned over the example style at different resolution and content. (3) We present novel self-supervised training criteria which preserve both source style and target content using a pre-trained font classifier and text recognizer. Finally, (4) we also introduce Imgur5K, a new challenging dataset for handwritten word images. We offer numerous qualitative photo-realistic results of our method. We further show that our method surpasses previous work in quantitative tests on scene text and handwriting datasets, as well as in a user study. Read More
#image-recognition, #gansVoilà
Voilà lets you turn selfies into caricatures, cartoons, and 18th-century paintings. This AI empowered photo editor is one of the top free apps available on GooglePlay and is also now available on the AppStore. Read More
Markpainting: Adversarial Machine Learning meets Inpainting
Inpainting is a learned interpolation technique that is based on generative modeling and used to populate masked or missing pieces in an image; it has wide applications in picture editing and retouching. Recently, inpainting started being used for watermark removal, raising concerns. In this paper we study how to manipulate it using our markpainting technique. First, we show how an image owner with access to an inpainting model can augment their image in such a way that any attempt to edit it using that model will add arbitrary visible information. We find that we can target multiple different models simultaneously with our technique. This can be designed to reconstitute a watermark if the editor had been trying to remove it. Second, we show that our markpainting technique is transferable to models that have different architectures or were trained on different datasets, so watermarks created using it are difficult for adversaries to remove. Markpainting is novel and can be used as a manipulation alarm that becomes visible in the event of inpainting. Read More
#adversarial, #image-recognitionBARF: Bundle-Adjusting Neural Radiance Fields
Neural Radiance Fields (NeRF) [30] have recently gained a surge of interest within the computer vision community for its power to synthesize photorealistic novel views of real-world scenes. One limitation of NeRF, however, is its requirement of accurate camera poses to learn the scene representations. In this paper, we propose Bundle-Adjusting Neural Radiance Fields (BARF) for training NeRF from imperfect (or even unknown) camera poses — the joint problem of learning neural 3D representations and registering cam-era frames. We establish a theoretical connection to classical image alignment and show that coarse-to-fine registration is also applicable to NeRF. Furthermore, we show that naïvely applying positional encoding in NeRF has a negative impact on registration with a synthesis-based objective. Experiments on synthetic and real-world data show that BARF can effectively optimize the neural scene representations and re-solve large camera pose misalignment at the same time. This enables view synthesis and localization of video sequences from unknown camera poses, opening up new avenues for visual localization systems (e.g. SLAM) and potential applications for dense 3D mapping and reconstruction. Read More
#image-recognitionAI system outperforms humans in designing floorplans for microchips
A machine-learning system has been trained to place memory blocks in microchip designs. The system beats human experts at the task, and offers the promise of better, more-rapidly produced chip designs than are currently possible.
Success or failure in designing microchips depends heavily on steps known as floorplanning and placement. These steps determine where memory and logic elements are located on a chip. The locations, in turn, strongly affect whether the completed chip design can satisfy operational requirements such as processing speed and power efficiency. So far, the floorplanning task, in particular, has defied all attempts at automation. It is therefore performed iteratively and painstakingly, over weeks or months, by expert human engineers. But in a paper in Nature, researchers from Google (Mirhoseini et al.1) report a machine-learning approach that achieves superior chip floorplanning in hours. Read More
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