Adversarial Scratches: Deployable Attacks to CNN Classifiers

1 code implementation • 20 Apr 2022 • Loris Giulivi, Malhar Jere, Loris Rossi, Farinaz Koushanfar, Gabriela Ciocarlie, Briland Hitaj, Giacomo Boracchi

We present Adversarial Scratches: a novel L0 black-box attack, which takes the form of scratches in images, and which possesses much greater deployability than other state-of-the-art attacks.

Scratch that! An Evolution-based Adversarial Attack against Neural Networks

1 code implementation • 5 Dec 2019 • Malhar Jere, Loris Rossi, Briland Hitaj, Gabriela Ciocarlie, Giacomo Boracchi, Farinaz Koushanfar

We study black-box adversarial attacks for image classifiers in a constrained threat model, where adversaries can only modify a small fraction of pixels in the form of scratches on an image.

Adversarial Attack Image Captioning +1

ct-fuzz: Fuzzing for Timing Leaks

no code implementations • 15 Apr 2019 • Shaobo He, Michael Emmi, Gabriela Ciocarlie

Testing-based methodologies like fuzzing are able to analyze complex software which is not amenable to traditional formal approaches like verification, model checking, and abstract interpretation.

Software Engineering