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:20240329T061433Z 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-21615@www.clsp.jhu.edu DTSTAMP:20240329T061433Z CATEGORIES;LANGUAGE=en-US:Student Seminars CONTACT: DESCRIPTION:Abstract\n\n\nWe consider a problem of data collection for sema ntically rich NLU tasks\, where detailed semantics of documents (or uttera nces) are captured using a complex meaning representation. Previously\, d ata collection for such tasks was either handled at the cost of extensive annotator training (e.g. in FrameNet or PropBank) or simplified meaning re presentation (e.g. in QA-SRL or Overnight). In this talk\, we present two systems [1\, 2] that aim to support fast\, accurate\, and expressive sema ntic annotations by pairing human workers with a trained model in the loop .\n\nThe first system\, called Guided K-best [1]\, is an annotation toolki t for conversational semantic parsing. Instead of typing annotations from scratch\, data specialists choose a correct parse from the K-best output of a few-shot prototyped model. As the K-best list can be large (e.g. K=1 00)\, we guide the annotators’ exploration of the K-best list via explaina ble hierarchical clustering. In addition\, we experiment with RoBERTa-bas ed reranking of the K-best list to recalibrate the few-shot model towards Accuracy@K. The final system allows to annotate data up to 35% faster tha n the standard\, non-guided K-best and improves the few-shot model’s top-1 accuracy by up to 18%. The second system\, called SchemaBlocks [2]\, is an annotation toolkit for schemas\, or structured descriptions of frequent real-world scenarios (e.g.\, cooking a meal). It represents schemas in t he annotation UI as nested blocks. Using a novel Causal ARM model\, we fu rther speed up the annotation process and guide data specialists towards e xpressive and diverse schemas. As part of this work\, we collect 232 sche mas\, evaluating their internal coherence and their coverage on large-scal e newswire corpora.\n\n\n DTSTART;TZID=America/New_York:20220311T120000 DTEND;TZID=America/New_York:20220311T131500 LOCATION:Virtual Seminar SEQUENCE:0 SUMMARY:Student Seminar – Anton Belyy “Systems for Human-AI Cooperation on Collecting Semantic Annotations” URL:https://www.clsp.jhu.edu/events/student-seminar-anton-belyy-systems-for -human-ai-cooperation-on-collecting-semantic-annotations/ X-COST-TYPE:free X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\nAbstr act
\n\n X-TAGS;LANGUAGE=en-US:2022\,Belyy\,March END:VEVENT BEGIN:VEVENT UID:ai1ec-24511@www.clsp.jhu.edu DTSTAMP:20240329T061433Z 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