6 papers
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 -…
Extraction of Complex DNN Models: Real Threat or Boogeyman?
Buse Gul Atli, Sebastian Szyller, Mika Juuti +2
Recently, machine learning (ML) has introduced advanced solutions to many domains. Since ML models provide business advantage to model owners, protecting intellectual property of M…
Making targeted black-box evasion attacks effective and efficient
Mika Juuti, Buse Gul Atli, N. Asokan
We investigate how an adversary can optimally use its query budget for targeted evasion attacks against deep neural networks in a black-box setting. We formalize the problem settin…
All You Need is "Love": Evading Hate-speech Detection
Tommi Gröndahl, Luca Pajola, Mika Juuti +2
With the spread of social networks and their unfortunate use for hate speech, automatic detection of the latter has become a pressing problem. In this paper, we reproduce seven sta…
Stay On-Topic: Generating Context-specific Fake Restaurant Reviews
Mika Juuti, Bo Sun, Tatsuya Mori +1
Automatically generated fake restaurant reviews are a threat to online review systems. Recent research has shown that users have difficulties in detecting machine-generated fake re…
PRADA: Protecting against DNN Model Stealing Attacks
Mika Juuti, Sebastian Szyller, Samuel Marchal +1
Machine learning (ML) applications are increasingly prevalent. Protecting the confidentiality of ML models becomes paramount for two reasons: (a) a model can be a business advantag…