Attacking Deep Learning-Based NLP Systems with Malicious Word Embeddings

Presented at BSidesSF 2019, March 3, 2019, 2:10 p.m. (30 minutes)

Recent Deep Learning-based Natural Language Processing (NLP) systems rely heavily on Word Embeddings, a.k.a. Word Vectors, a method of converting words into meaningful vectors of numbers. However, the process of gathering data, training word embeddings, and incorporating them into an NLP system has received little scrutiny from a security perspective. In this talk we demonstrate that we can influence such systems by manipulating training data and how we can inject them into real-world systems.

Presenters:

• Toshiro Nishimura
  Toshiro is an independent software engineer and entrepreneur with a passion for security and privacy. Previously he has worked in email security and bioinformatics. His regular expression and Vim skills are second to none.

Links:

• https://bsidessf2019.sched.com/event/JddB/attacking-deep-learning-based-nlp-systems-with-malicious-word-embeddings

Similar Presentations:

• RomHack 2019 / SAFE: Self Attentive Function Embedding for Binary Similarity
• BSidesLV 2019 / Evaluating Code Embeddings
• 33C3 (2016) / A Story of Discrimination and Unfairness: Prejudice in Word Embeddings