13 citations · 24 across the 6 of their papers we have counts for
6 papers
Attesting Distributional Properties of Training Data for Machine Learning
Vasisht Duddu, Anudeep Das, Nora Khayata +3
The success of machine learning (ML) has been accompanied by increased concerns about its trustworthiness. Several jurisdictions are preparing ML regulatory frameworks. One such co…
False Claims against Model Ownership Resolution
Jian Liu, Rui Zhang, Sebastian Szyller +2
Deep neural network (DNN) models are valuable intellectual property of model owners, constituting a competitive advantage. Therefore, it is crucial to develop techniques to protect…
Is GitHub's Copilot as Bad as Humans at Introducing Vulnerabilities in Code?
Owura Asare, Meiyappan Nagappan, N. Asokan
Several advances in deep learning have been successfully applied to the software development process. Of recent interest is the use of neural language models to build tools, such a…
Do Transformers know symbolic rules, and would we know if they did?
Tommi Gröndahl, Yujia Guo, N. Asokan
To improve the explainability of leading Transformer networks used in NLP, it is important to tease apart genuine symbolic rules from merely associative input-output patterns. Howe…
Good Artists Copy, Great Artists Steal: Model Extraction Attacks Against Image Translation Models
Sebastian Szyller, Vasisht Duddu, Tommi Gröndahl +1
Machine learning models are typically made available to potential client users via inference APIs. Model extraction attacks occur when a malicious client uses information gleaned f…
A little goes a long way: Improving toxic language classification despite data scarcity
Mika Juuti, Tommi Gröndahl, Adrian Flanagan +1
Detection of some types of toxic language is hampered by extreme scarcity of labeled training data. Data augmentation - generating new synthetic data from a labeled seed dataset -…