270 citations · 444 across the 14 of their papers we have counts for
6 papers · 1 filter
Adversarial Semantic Collisions
Congzheng Song, Alexander M. Rush, Vitaly Shmatikov
We study semantic collisions: texts that are semantically unrelated but judged as similar by NLP models. We develop gradient-based approaches for generating semantic collisions and…
You Autocomplete Me: Poisoning Vulnerabilities in Neural Code Completion
Roei Schuster, Congzheng Song, Eran Tromer +1
Code autocompletion is an integral feature of modern code editors and IDEs. The latest generation of autocompleters uses neural language models, trained on public open-source code…
De-Anonymizing Text by Fingerprinting Language Generation
Zhen Sun, Roei Schuster, Vitaly Shmatikov
Components of machine learning systems are not (yet) perceived as security hotspots. Secure coding practices, such as ensuring that no execution paths depend on confidential inputs…
Blind Backdoors in Deep Learning Models
Eugene Bagdasaryan, Vitaly Shmatikov
We investigate a new method for injecting backdoors into machine learning models, based on compromising the loss-value computation in the model-training code. We use it to demonstr…
Salvaging Federated Learning by Local Adaptation
Tao Yu, Eugene Bagdasaryan, Vitaly Shmatikov
Federated learning (FL) is a heavily promoted approach for training ML models on sensitive data, e.g., text typed by users on their smartphones. FL is expressly designed for traini…
Humpty Dumpty: Controlling Word Meanings via Corpus Poisoning
Roei Schuster, Tal Schuster, Yoav Meri +1
Word embeddings, i.e., low-dimensional vector representations such as GloVe and SGNS, encode word "meaning" in the sense that distances between words' vectors correspond to their s…