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The advent of larger machine learning (ML) models have improved state-of-the-art (SOTA) performance in various modeling tasks, ranging from computer vision to natural language. As ML models continue increasing in size, so does their respective energy consumption and computational requirements. However, the methods for tracking, reporting, and comparing energy consumption remain limited. We present EnergyVis, an interactive energy consumption tracker for ML models. Consisting of multiple coordinated views, EnergyVis enables researchers to interactively track, visualize and compare model energy consumption across key energy consumption and carbon footprint metrics (kWh and CO2), helping users explore alternative deployment locations and hardware that may reduce carbon footprints. EnergyVis aims to raise awareness concerning computational sustainability by interactively highlighting excessive energy usage during model training; and by providing alternative training options to reduce energy usage. 

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. Code related to this paper is available at: https://github.com/nilakshdas/ADAGIO. 

Research in the upcoming field of adversarial ML has revealed that machine learning, especially deep learning, is highly vulnerable to imperceptible adversarial perturbations, both in the domain of vision as well as speech. This has induced an urgent need to devise fast and practical approaches to secure deep learning models from adversarial attacks, so that they can be safely deployed in real-world applications. In this showcase, we put forth the idea of compression as a viable solution to defend against adversarial attacks across modalities. Since most of these attacks depend on the gradient of the model to craft an adversarial instance, compression, which is usually non-differentiable, denies a useful gradient to the attacker. In the vision domain we have JPEG compression, and in the audio domain we have MP3 compression and AMR encoding -- all widely adopted techniques that have very fast implementations on most platforms, and can be feasibly leveraged as defenses. We will show the effectiveness of these techniques against adversarial attacks through live demonstrations, both for vision as well as speech. These demonstrations would include real-time computation of adversarial perturbations for images and audio, as well as interactive application of compression for defense. We would invite and encourage the audience to experiment with their own images and audio samples during the demonstrations. This work was undertaken jointly by researchers from Georgia Institute of Technology and Intel Corporation.