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A Control SidebarFigure 1: With BLUFF, users interactively visualize how adversarial attacks penetrate a deep neural network to induce incorrect outcomes. Here, a user inspects why INCEPTIONV1 misclassifies adversarial giant panda images, crafted by the Projected Gradient Descent (PGD) attack, as armadillo. PGD successfully perturbed pixels to induce the "brown bird" feature, an appearance more likely shared by an armadillo (small, roundish, brown body) than a panda, activating more features that contribute to the armadillo (mis)classification (e.g., "scales," "bumps," "mesh"). The adversarial pathways, formed by these neurons and their connections, overwhelm the benign panda pathways and lead to the ultimate misclassification. (A) Control Sidebar allows users to specify what data is to be included and highlighted. (B) Graph Summary View visualizes pathways most activated or changed by an attack as a network graph of neurons (each labeled by the channel ID in its layer) and their connections. When hovering over a neuron, (C) Detail View displays its feature visualization, representative dataset examples, and activation patterns over attack strengths.

Generating large labeled training data is becoming the biggest bottleneck in building and deploying supervised machine learning models. Recently, the data programming paradigm has been proposed to reduce the human cost in labeling training data. However, data programming relies on designing labeling functions which still requires significant domain expertise. Also, it is prohibitively difficult to write labeling functions for image datasets as it is hard to express domain knowledge using raw features for images (pixels).We propose affinity coding, a new domain-agnostic paradigm for automated training data labeling. The core premise of affinity coding is that the affinity scores of instance pairs belonging to the same class on average should be higher than those of pairs belonging to different classes, according to some affinity functions. We build the GOGGLES system that implements affinity coding for labeling image datasets by designing a novel set of reusable affinity functions for images, and propose a novel hierarchical generative model for class inference using a small development set.We compare GOGGLES with existing data programming systems on 5 image labeling tasks from diverse domains. GOGGLES achieves labeling accuracies ranging from a minimum of 71% to a maximum of 98% without requiring any extensive human annotation. In terms of end-to-end performance, GOGGLES outperforms the state-of-the-art data programming system Snuba by 21% and a state-of-the-art

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Adversarial machine learning research has recently demonstrated the feasibility to confuse automatic speech recognition (ASR) models by introducing acoustically imperceptible perturbations to audio samples. To help researchers and practitioners gain better understanding of the impact of such attacks, and to provide them with tools to help them more easily evaluate and craft strong defenses for their models, we present Adagio, the first tool designed to allow interactive experimentation with adversarial attacks and defenses on an ASR model in real time, both visually and aurally. Adagio incorporates AMR and MP3 audio compression techniques as defenses, which users can interactively apply to attacked audio samples. We show that these techniques, which are based on psychoacoustic principles, effectively eliminate targeted attacks, reducing the attack success rate from 92.5% to 0%. We will demonstrate Adagio and invite the audience to try it on the Mozilla Common Voice dataset.