BEGIN:VCALENDAR VERSION:2.0 PRODID:-//128.220.36.25//NONSGML kigkonsult.se iCalcreator 2.26.9// CALSCALE:GREGORIAN METHOD:PUBLISH X-FROM-URL:https://www.clsp.jhu.edu X-WR-TIMEZONE:America/New_York BEGIN:VTIMEZONE TZID:America/New_York X-LIC-LOCATION:America/New_York BEGIN:STANDARD DTSTART:20231105T020000 TZOFFSETFROM:-0400 TZOFFSETTO:-0500 RDATE:20241103T020000 TZNAME:EST END:STANDARD BEGIN:DAYLIGHT DTSTART:20240310T020000 TZOFFSETFROM:-0500 TZOFFSETTO:-0400 RDATE:20250309T020000 TZNAME:EDT END:DAYLIGHT END:VTIMEZONE BEGIN:VEVENT UID:ai1ec-21494@www.clsp.jhu.edu DTSTAMP:20240329T133830Z CATEGORIES;LANGUAGE=en-US:Student Seminars CONTACT: DESCRIPTION:
Abstract
\nAdversarial atta cks deceive neural network systems by adding carefully crafted perturbatio ns to benign signals. Being almost imperceptible to humans\, these attacks pose a severe security threat to the state-of-the-art speech and speaker recognition systems\, making it vital to propose countermeasures against t hem. In this talk\, we focus on 1) classification of a given adversarial a ttack into attack algorithm type\, threat model type\, and signal-to-adver sarial-noise ratios\, 2) developing a novel speech denoising solution to f urther improve the classification performance.
\nO ur proposed approach uses an x-vector network as a signature extractor to get embeddings\, which we call signatures. These signatures contain inform ation about the attack and can help classify different attack algorithms\, threat models\, and signal-to-adversarial-noise ratios. We demonstrate th e transferability of such signatures to other tasks. In particular\, a sig nature extractor trained to classify attacks against speaker identificatio n can also be used to classify attacks against speaker verification and sp eech recognition. We also show that signatures can be used to detect unkno wn attacks i.e. attacks not included during training. Lastly\, we propose to improve the signature extractor by making the job of the signature ext ractor easier by removing the clean signal from the adversarial example (w hich consists of clean signal+perturbation). We train our signature extrac tor using adversarial perturbation. At inference time\, we use a time-doma in denoiser to obtain adversarial perturbation from adversarial examples. Using our improved approach\, we show that common attacks in the literatur e (Fast Gradient Sign Method (FGSM)\, Projected Gradient Descent (PGD)\, C arlini-Wagner (CW) ) can be classified with accuracy as high as 96%. We al so detect unknown attacks with an equal error rate (EER) of about 9%\, whi ch is very promising.
DTSTART;TZID=America/New_York:20220304T120000 DTEND;TZID=America/New_York:20220304T131500 LOCATION:Ames Hall 234 @ 3400 N. Charles Street\, Baltimore\, MD 21218 SEQUENCE:0 SUMMARY:Student Seminar – Sonal Joshi “Classify and Detect Adversarial Atta cks Against Speaker and Speech Recognition Systems” URL:https://www.clsp.jhu.edu/events/student-seminar-sonal-joshi/ X-COST-TYPE:free X-TAGS;LANGUAGE=en-US:2022\,Joshi\,March END:VEVENT BEGIN:VEVENT UID:ai1ec-22412@www.clsp.jhu.edu DTSTAMP:20240329T133830Z CATEGORIES;LANGUAGE=en-US:Seminars CONTACT: DESCRIPTION:Abstract
\nDriven by the goal of erad icating language barriers on a global scale\, machine translation has soli dified itself as a key focus of artificial intelligence research today. Ho wever\, such efforts have coalesced around a small subset of languages\, l eaving behind the vast majority of mostly low-resource languages. What doe s it take to break the 200 language barrier while ensuring safe\, high-qua lity results\, all while keeping ethical considerations in mind? In this t alk\, I introduce No Language Left Behind\, an initiative to break languag e barriers for low-resource languages. In No Language Left Behind\, we too k on the low-resource language translation challenge by first contextualiz ing the need for translation support through exploratory interviews with n ative speakers. Then\, we created datasets and models aimed at narrowing t he performance gap between low and high-resource languages. We proposed mu ltiple architectural and training improvements to counteract overfitting w hile training on thousands of tasks. Critically\, we evaluated the perform ance of over 40\,000 different translation directions using a human-transl ated benchmark\, Flores-200\, and combined human evaluation with a novel t oxicity benchmark covering all languages in Flores-200 to assess translati on safety. Our model achieves an improvement of 44% BLEU relative to the p revious state-of-the-art\, laying important groundwork towards realizing a universal translation system in an open-source manner.
\nBi ography
\nAngela is a research scientis t at Meta AI Research in New York\, focusing on supporting efforts in spee ch and language research. Recent projects include No Language Left Behind (https://ai.facebook.com/r esearch/no-language-left-behind/) and Universal Speech Translation for Unwritten Languages (https://ai.faceb ook.com/blog/ai-translation-hokkien/). Before translation\, Angela pre viously focused on research in on-device models for NLP and computer visio n and text generation.
\nDTSTART;TZID=America/New_York:20221118T120000 DTEND;TZID=America/New_York:20221118T131500 LOCATION:Hackerman Hall B17 @ 3400 N. Charles Street\, Baltimore\, MD 21218 SEQUENCE:0 SUMMARY:Angela Fan (Meta AI Research) “No Language Left Behind: Scaling Hu man-Centered Machine Translation” URL:https://www.clsp.jhu.edu/events/angela-fan-facebook/ X-COST-TYPE:free X-TAGS;LANGUAGE=en-US:2022\,Fan\,November END:VEVENT BEGIN:VEVENT UID:ai1ec-24511@www.clsp.jhu.edu DTSTAMP:20240329T133830Z CATEGORIES;LANGUAGE=en-US:Student Seminars CONTACT: DESCRIPTION: DTSTART;TZID=America/New_York:20240412T120000 DTEND;TZID=America/New_York:20240412T131500 LOCATION:Hackerman Hall B17 @ 3400 N. Charles Street\, Baltimore\, MD 21218 SEQUENCE:0 SUMMARY:Sonal Joshi (JHU) URL:https://www.clsp.jhu.edu/events/sonal-joshi-jhu/ X-COST-TYPE:free X-TAGS;LANGUAGE=en-US:2024\,April\,Joshi END:VEVENT END:VCALENDAR