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:20240329T131812Z CATEGORIES;LANGUAGE=en-US:Student Seminars CONTACT: DESCRIPTION:Abstract\nAdversarial attacks deceive neural network systems by adding carefully crafted perturbations to benign signals. Being almost im perceptible 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 them. In this talk\, we focus on 1) cl assification of a given adversarial attack into attack algorithm type\, th reat model type\, and signal-to-adversarial-noise ratios\, 2) developing a novel speech denoising solution to further improve the classification per formance. \nOur proposed approach uses an x-vector network as a signature extractor to get embeddings\, which we call signatures. These signatures c ontain information about the attack and can help classify different attack algorithms\, threat models\, and signal-to-adversarial-noise ratios. We d emonstrate the transferability of such signatures to other tasks. In parti cular\, a signature extractor trained to classify attacks against speaker identification can also be used to classify attacks against speaker verifi cation and speech recognition. We also show that signatures can be used to detect unknown attacks i.e. attacks not included during training. Lastly \, we propose to improve the signature extractor by making the job of the signature extractor easier by removing the clean signal from the adversari al example (which consists of clean signal+perturbation). We train our sig nature extractor using adversarial perturbation. At inference time\, we us e a time-domain denoiser to obtain adversarial perturbation from adversari al examples. Using our improved approach\, we show that common attacks in the literature (Fast Gradient Sign Method (FGSM)\, Projected Gradient Desc ent (PGD)\, Carlini-Wagner (CW) ) can be classified with accuracy as high as 96%. We also detect unknown attacks with an equal error rate (EER) of a bout 9%\, which 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-ALT-DESC;FMTTYPE=text/html:\\n\\n
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\nAdversarial attacks deceive neural network systems by adding carefully crafted perturbations to benign signals. Being almost imperceptible to humans\, these attacks pose a severe security thr eat to the state-of-the-art speech and speaker recognition systems\, makin g it vital to propose countermeasures against them. In this talk\, we focu s on 1) classification of a given adversarial attack into attack algorithm type\, threat model type\, and signal-to-adversarial-noise ratios\, 2) de veloping a novel speech denoising solution to further improve the classifi cation performance.
\nOur proposed approach uses a n x-vector network as a signature extractor to get embeddings\, which we c all signatures. These signatures contain information about the attack and can help classify different attack algorithms\, threat models\, and signal -to-adversarial-noise ratios. We demonstrate the transferability of such s ignatures to other tasks. In particular\, a signature extractor trained to classify attacks against speaker identification can also be used to class ify attacks against speaker verification and speech recognition. We also s how that signatures can be used to detect unknown attacks i.e. attacks not included during training. Lastly\, we propose to improve the signature e xtractor by making the job of the signature extractor easier by removing t he clean signal from the adversarial example (which consists of clean sign al+perturbation). We train our signature extractor using adversarial pertu rbation. At inference time\, we use a time-domain denoiser to obtain adver sarial perturbation from adversarial examples. Using our improved approach \, we show that common attacks in the literature (Fast Gradient Sign Metho d (FGSM)\, Projected Gradient Descent (PGD)\, Carlini-Wagner (CW) ) can be classified with accuracy as high as 96%. We also detect unknown attacks w ith an equal error rate (EER) of about 9%\, which is very promising.
\n X-TAGS;LANGUAGE=en-US:2022\,Joshi\,March END:VEVENT BEGIN:VEVENT UID:ai1ec-22394@www.clsp.jhu.edu DTSTAMP:20240329T131812Z CATEGORIES;LANGUAGE=en-US:Seminars CONTACT: DESCRIPTION:Abstract\n\nModel robustness and spurious correlations have rec eived increasing attention in the NLP community\, both in methods and eval uation. The term “spurious correlation” is overloaded though and can refer to any undesirable shortcuts learned by the model\, as judged by domain e xperts.\n\n\nWhen designing mitigation algorithms\, we often (implicitly) assume that a spurious feature is irrelevant for prediction. However\, man y features in NLP (e.g. word overlap and negation) are not spurious in the sense that the background is spurious for classifying objects in an image . In contrast\, they carry important information that’s needed to make pre dictions by humans. In this talk\, we argue that it is more productive to characterize features in terms of their necessity and sufficiency for pred iction. We then discuss the implications of this categorization in represe ntation\, learning\, and evaluation.\nBiography\nHe He is an Assistant Pro fessor in the Department of Computer Science and the Center for Data Scien ce at New York University. She obtained her PhD in Computer Science at the University of Maryland\, College Park. Before joining NYU\, she spent a y ear at AWS AI and was a post-doc at Stanford University before that. She i s interested in building robust and trustworthy NLP systems in human-cente red settings. Her recent research focus includes robust language understan ding\, collaborative text generation\, and understanding capabilities and issues of large language models. DTSTART;TZID=America/New_York:20221014T120000 DTEND;TZID=America/New_York:20221014T131500 LOCATION:Hackerman Hall B17 @ 3400 N. Charles Street\, Baltimore\, MD 21218 SEQUENCE:0 SUMMARY:He He (New York University) “What We Talk about When We Talk about Spurious Correlations in NLP” URL:https://www.clsp.jhu.edu/events/he-he-new-york-university/ X-COST-TYPE:free X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\nAbstr act
\nModel robustness and spuri ous correlations have received increasing attention in the NLP community\, both in methods and evaluation. The term “spurious correlation” is overlo aded though and can refer to any undesirable shortcuts learned by the mode l\, as judged by domain experts.
\nWhen designing mitigation algorithms\, we often (implicitly) assume that a spurious feature is irrelevant for prediction. However\, many features in NLP (e.g. word overlap and negation) are not spurious in the sense that the background is spurious for classifying objects in an image. In contra st\, they carry important information that’s needed to make predictions by humans. In this talk\, we argue that it is more productive to characteriz e features in terms of their necessity and sufficiency for prediction. We then discuss the implications of this categorization in representation\, l earning\, and evaluation.
\nBiography
\nHe He is an Assistant Professor in the Department of Computer Science and the C enter for Data Science at New York University. She obtained her PhD in Com puter Science at the University of Maryland\, College Park. Before joining NYU\, she spent a year at AWS AI and was a post-doc at Stanford Universit y before that. She is interested in building robust and trustworthy NLP sy stems in human-centered settings. Her recent research focus includes robus t language understanding\, collaborative text generation\, and understandi ng capabilities and issues of large language models.
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