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Course Level Date Clip# Line Interlocutor Content
Oral Assessments Graduate 2019-08-14 3 76 S01 one of the fa- ☺ahem☺ what adversarial machine
Oral Assessments Graduate 2019-08-14 3 77 S01 learning do is uh they are trying uh they are
Oral Assessments Graduate 2019-08-14 3 78 S01 trying to (0.4) fool the (0.3) the machine learning
Oral Assessments Graduate 2019-08-14 3 79 S01 models through malicious inputs such as add some
Oral Assessments Graduate 2019-08-14 3 80 S01 noise to the paper image of cat and make the: mm mm↗
Oral Assessments Graduate 2019-08-14 3 81 T02 i'm sorry what is male- what do you mean by malicious↗
Oral Assessments Graduate 2019-08-14 3 82 S01 uh malicious means (.) for example the ☉benign☉ (.)
Oral Assessments Graduate 2019-08-14 3 83 S01 uh (.) image is a: is a normal: cat image ≈
Oral Assessments Graduate 2019-08-14 3 84 T02 +≈ okay→
Oral Assessments Graduate 2019-08-14 3 85 S01 and we add some (.) specially designed noise↘
Oral Assessments Graduate 2019-08-14 3 86 UNK (0.5)
Oral Assessments Graduate 2019-08-14 3 87 S01 uh they are (0.6) very irregular from our humans
Oral Assessments Graduate 2019-08-14 3 88 S01 vision↘ but a- after adding this noise→ the (.) uh
Oral Assessments Graduate 2019-08-14 3 89 S01 the image becomes malicious that will be misclassified
Oral Assessments Graduate 2019-08-14 3 90 S01 by the: (0.4) ∆machine learning model∆↘
Oral Assessments Graduate 2019-08-14 3 91 S01 oh okay
Oral Assessments Graduate 2019-08-14 3 92 S01 yeah↘
Oral Assessments Graduate 2019-08-14 3 93 S01 uh okay→ so let's continue↘ uh (1.5) &hehehehe
Oral Assessments Graduate 2019-08-14 3 94 S01 u:h (0.8) ahem (0.7) and uh what ∆adversarial machine∆
Oral Assessments Graduate 2019-08-14 3 95 S01 ∆learning do∆ is ju- try to add some noise to
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