Search Results for author: Briland Hitaj

Found 9 papers, 4 papers with code

Trust in Motion: Capturing Trust Ascendancy in Open-Source Projects using Hybrid AI

no code implementations6 Oct 2022 Huascar Sanchez, Briland Hitaj

We refer to this process of influence-seeking and trust-building as trust ascendancy.

Adversarial Scratches: Deployable Attacks to CNN Classifiers

1 code implementation20 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.

FedComm: Federated Learning as a Medium for Covert Communication

no code implementations21 Jan 2022 Dorjan Hitaj, Giulio Pagnotta, Briland Hitaj, Fernando Perez-Cruz, Luigi V. Mancini

Proposed as a solution to mitigate the privacy implications related to the adoption of deep learning, Federated Learning (FL) enables large numbers of participants to successfully train deep neural networks without having to reveal the actual private training data.

Federated Learning

Capture the Bot: Using Adversarial Examples to Improve CAPTCHA Robustness to Bot Attacks

no code implementations30 Oct 2020 Dorjan Hitaj, Briland Hitaj, Sushil Jajodia, Luigi V. Mancini

To this date, CAPTCHAs have served as the first line of defense preventing unauthorized access by (malicious) bots to web-based services, while at the same time maintaining a trouble-free experience for human visitors.

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

1 code implementation5 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

WHAT ARE GANS USEFUL FOR?

no code implementations ICLR 2018 Pablo M. Olmos, Briland Hitaj, Paolo Gasti, Giuseppe Ateniese, Fernando Perez-Cruz

In this paper, we noticed that even though GANs might not be able to generate samples from the underlying distribution (or we cannot tell at least), they are capturing some structure of the data in that high dimensional space.

Density Estimation

PassGAN: A Deep Learning Approach for Password Guessing

3 code implementations1 Sep 2017 Briland Hitaj, Paolo Gasti, Giuseppe Ateniese, Fernando Perez-Cruz

State-of-the-art password guessing tools, such as HashCat and John the Ripper, enable users to check billions of passwords per second against password hashes.

BIG-bench Machine Learning

Deep Models Under the GAN: Information Leakage from Collaborative Deep Learning

1 code implementation24 Feb 2017 Briland Hitaj, Giuseppe Ateniese, Fernando Perez-Cruz

Unfortunately, we show that any privacy-preserving collaborative deep learning is susceptible to a powerful attack that we devise in this paper.

Privacy Preserving

Cannot find the paper you are looking for? You can Submit a new open access paper.