Search Results for author: Ali Shahin Shamsabadi

Found 14 papers, 10 papers with code

Semantically Adversarial Learnable Filters

1 code implementation13 Aug 2020 Ali Shahin Shamsabadi, Changjae Oh, Andrea Cavallaro

The semantic adversarial loss considers groups of (semantic) labels to craft perturbations that prevent the filtered image being classified with a label in the same group.

Exploiting vulnerabilities of deep neural networks for privacy protection

1 code implementation19 Jul 2020 Ricardo Sanchez-Matilla, Chau Yi Li, Ali Shahin Shamsabadi, Riccardo Mazzon, Andrea Cavallaro

To address these limitations, we present an adversarial attack {that is} specifically designed to protect visual content against { unseen} classifiers and known defenses.

Adversarial Attack Quantization

DarkneTZ: Towards Model Privacy at the Edge using Trusted Execution Environments

2 code implementations12 Apr 2020 Fan Mo, Ali Shahin Shamsabadi, Kleomenis Katevas, Soteris Demetriou, Ilias Leontiadis, Andrea Cavallaro, Hamed Haddadi

We present DarkneTZ, a framework that uses an edge device's Trusted Execution Environment (TEE) in conjunction with model partitioning to limit the attack surface against Deep Neural Networks (DNNs).

Image Classification

PrivEdge: From Local to Distributed Private Training and Prediction

1 code implementation12 Apr 2020 Ali Shahin Shamsabadi, Adria Gascon, Hamed Haddadi, Andrea Cavallaro

To address this problem, we propose PrivEdge, a technique for privacy-preserving MLaaS that safeguards the privacy of users who provide their data for training, as well as users who use the prediction service.

Image Compression

ColorFool: Semantic Adversarial Colorization

1 code implementation CVPR 2020 Ali Shahin Shamsabadi, Ricardo Sanchez-Matilla, Andrea Cavallaro

Instead, adversarial attacks that generate unrestricted perturbations are more robust to defenses, are generally more successful in black-box settings and are more transferable to unseen classifiers.

Adversarial Attack Colorization +1

EdgeFool: An Adversarial Image Enhancement Filter

1 code implementation27 Oct 2019 Ali Shahin Shamsabadi, Changjae Oh, Andrea Cavallaro

This loss function accounts for both image detail enhancement and class misleading objectives.

Denoising Image Enhancement

Towards Characterizing and Limiting Information Exposure in DNN Layers

no code implementations13 Jul 2019 Fan Mo, Ali Shahin Shamsabadi, Kleomenis Katevas, Andrea Cavallaro, Hamed Haddadi

Pre-trained Deep Neural Network (DNN) models are increasingly used in smartphones and other user devices to enable prediction services, leading to potential disclosures of (sensitive) information from training data captured inside these models.

QUOTIENT: Two-Party Secure Neural Network Training and Prediction

no code implementations8 Jul 2019 Nitin Agrawal, Ali Shahin Shamsabadi, Matt J. Kusner, Adrià Gascón

In this work, we investigate the advantages of designing training algorithms alongside a novel secure protocol, incorporating optimizations on both fronts.

Distributed One-class Learning

no code implementations10 Feb 2018 Ali Shahin Shamsabadi, Hamed Haddadi, Andrea Cavallaro

A major advantage of the proposed filter over existing distributed learning approaches is that users cannot access, even indirectly, the parameters of other users.

One-class classifier

Deep Private-Feature Extraction

1 code implementation9 Feb 2018 Seyed Ali Osia, Ali Taheri, Ali Shahin Shamsabadi, Kleomenis Katevas, Hamed Haddadi, Hamid R. Rabiee

We present and evaluate Deep Private-Feature Extractor (DPFE), a deep model which is trained and evaluated based on information theoretic constraints.

Privacy-Preserving Deep Inference for Rich User Data on The Cloud

1 code implementation4 Oct 2017 Seyed Ali Osia, Ali Shahin Shamsabadi, Ali Taheri, Kleomenis Katevas, Hamid R. Rabiee, Nicholas D. Lane, Hamed Haddadi

Our evaluations show that by using certain kind of fine-tuning and embedding techniques and at a small processing costs, we can greatly reduce the level of information available to unintended tasks applied to the data feature on the cloud, and hence achieving the desired tradeoff between privacy and performance.

A Hybrid Deep Learning Architecture for Privacy-Preserving Mobile Analytics

1 code implementation8 Mar 2017 Seyed Ali Osia, Ali Shahin Shamsabadi, Sina Sajadmanesh, Ali Taheri, Kleomenis Katevas, Hamid R. Rabiee, Nicholas D. Lane, Hamed Haddadi

To this end, instead of performing the whole operation on the cloud, we let an IoT device to run the initial layers of the neural network, and then send the output to the cloud to feed the remaining layers and produce the final result.

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