Search Results for author: Radha Poovendran

Found 26 papers, 5 papers with code

Game of Trojans: A Submodular Byzantine Approach

no code implementations13 Jul 2022 Dinuka Sahabandu, Arezoo Rajabi, Luyao Niu, Bo Li, Bhaskar Ramasubramanian, Radha Poovendran

The results show that (i) with Submodular Trojan algorithm, the adversary needs to embed a Trojan trigger into a very small fraction of samples to achieve high accuracy on both Trojan and clean samples, and (ii) the MM Trojan algorithm yields a trained Trojan model that evades detection with probability 1.

A Natural Language Processing Approach for Instruction Set Architecture Identification

no code implementations13 Apr 2022 Dinuka Sahabandu, Sukarno Mertoguno, Radha Poovendran

Empirical evaluations show that using our byte-level features in ML-based ISA identification results in an 8% higher accuracy than the state-of-the-art features based on byte-histograms and byte pattern signatures.

Malware Detection

Privacy-Preserving Reinforcement Learning Beyond Expectation

no code implementations18 Mar 2022 Arezoo Rajabi, Bhaskar Ramasubramanian, Abdullah Al Maruf, Radha Poovendran

Through empirical evaluations, we highlight a privacy-utility tradeoff and demonstrate that the RL agent is able to learn behaviors that are aligned with that of a human user in the same environment in a privacy-preserving manner

Decision Making Privacy Preserving +1

Shaping Advice in Deep Reinforcement Learning

1 code implementation19 Feb 2022 Baicen Xiao, Bhaskar Ramasubramanian, Radha Poovendran

We design two algorithms- Shaping Advice in Single-agent reinforcement learning (SAS) and Shaping Advice in Multi-agent reinforcement learning (SAM).

Multi-agent Reinforcement Learning reinforcement-learning

Agent-Temporal Attention for Reward Redistribution in Episodic Multi-Agent Reinforcement Learning

1 code implementation12 Jan 2022 Baicen Xiao, Bhaskar Ramasubramanian, Radha Poovendran

In this paper, we introduce Agent-Temporal Attention for Reward Redistribution in Episodic Multi-Agent Reinforcement Learning (AREL) to address these two challenges.

Multi-agent Reinforcement Learning reinforcement-learning +1

A Game-Theoretic Framework for Controlled Islanding in the Presence of Adversaries

no code implementations3 Aug 2021 Luyao Niu, Dinuka Sahabandu, Andrew Clark, Radha Poovendran

In this paper, we study the controlled islanding problem of a power system under disturbances introduced by a malicious adversary.

Reinforcement Learning Beyond Expectation

no code implementations29 Mar 2021 Bhaskar Ramasubramanian, Luyao Niu, Andrew Clark, Radha Poovendran

In this paper, we consider a setting where an autonomous agent has to learn behaviors in an unknown environment.

reinforcement-learning

Shaping Advice in Deep Multi-Agent Reinforcement Learning

1 code implementation29 Mar 2021 Baicen Xiao, Bhaskar Ramasubramanian, Radha Poovendran

We observe that using SAM results in agents learning policies to complete tasks faster, and obtain higher rewards than: i) using sparse rewards alone; ii) a state-of-the-art reward redistribution method.

Multi-agent Reinforcement Learning reinforcement-learning

Safety-Critical Online Control with Adversarial Disturbances

no code implementations20 Sep 2020 Bhaskar Ramasubramanian, Baicen Xiao, Linda Bushnell, Radha Poovendran

We propose an iterative approach to the synthesis of the controller by solving a modified discrete-time Riccati equation.

Stochastic Dynamic Information Flow Tracking Game using Supervised Learning for Detecting Advanced Persistent Threats

1 code implementation24 Jul 2020 Shana Moothedath, Dinuka Sahabandu, Joey Allen, Linda Bushnell, Wenke Lee, Radha Poovendran

Our game model has imperfect information as the players do not have information about the actions of the opponent.

Computer Science and Game Theory Cryptography and Security

FRESH: Interactive Reward Shaping in High-Dimensional State Spaces using Human Feedback

no code implementations19 Jan 2020 Baicen Xiao, Qifan Lu, Bhaskar Ramasubramanian, Andrew Clark, Linda Bushnell, Radha Poovendran

The output of the feedback neural network is converted to a shaping reward that is augmented to the reward provided by the environment.

Atari Games reinforcement-learning

Are Odds Really Odd? Bypassing Statistical Detection of Adversarial Examples

no code implementations28 Jul 2019 Hossein Hosseini, Sreeram Kannan, Radha Poovendran

In this paper, we first develop a classifier-based adaptation of the statistical test method and show that it improves the detection performance.

Potential-Based Advice for Stochastic Policy Learning

no code implementations20 Jul 2019 Baicen Xiao, Bhaskar Ramasubramanian, Andrew Clark, Hannaneh Hajishirzi, Linda Bushnell, Radha Poovendran

This paper augments the reward received by a reinforcement learning agent with potential functions in order to help the agent learn (possibly stochastic) optimal policies.

Q-Learning

Assessing Shape Bias Property of Convolutional Neural Networks

no code implementations21 Mar 2018 Hossein Hosseini, Baicen Xiao, Mayoore Jaiswal, Radha Poovendran

In order to conduct large scale experiments, we propose using the model accuracy on images with reversed brightness as a metric to evaluate the shape bias property.

One-Shot Learning

Semantic Adversarial Examples

1 code implementation16 Mar 2018 Hossein Hosseini, Radha Poovendran

This property is used by several defense methods to counter adversarial examples by applying denoising filters or training the model to be robust to small perturbations.

Denoising

Google's Cloud Vision API Is Not Robust To Noise

no code implementations16 Apr 2017 Hossein Hosseini, Baicen Xiao, Radha Poovendran

For example, an adversary can bypass an image filtering system by adding noise to inappropriate images.

Deceiving Google's Cloud Video Intelligence API Built for Summarizing Videos

no code implementations26 Mar 2017 Hossein Hosseini, Baicen Xiao, Radha Poovendran

For this, we select an image, which is different from the video content, and insert it, periodically and at a very low rate, into the video.

Image Classification

On the Limitation of Convolutional Neural Networks in Recognizing Negative Images

no code implementations20 Mar 2017 Hossein Hosseini, Baicen Xiao, Mayoore Jaiswal, Radha Poovendran

To this end, we evaluate CNNs on negative images, since they share the same structure and semantics as regular images and humans can classify them correctly.

Blocking Transferability of Adversarial Examples in Black-Box Learning Systems

no code implementations13 Mar 2017 Hossein Hosseini, Yize Chen, Sreeram Kannan, Baosen Zhang, Radha Poovendran

Advances in Machine Learning (ML) have led to its adoption as an integral component in many applications, including banking, medical diagnosis, and driverless cars.

Medical Diagnosis

Deceiving Google's Perspective API Built for Detecting Toxic Comments

no code implementations27 Feb 2017 Hossein Hosseini, Sreeram Kannan, Baosen Zhang, Radha Poovendran

In this paper, we propose an attack on the Perspective toxic detection system based on the adversarial examples.

Learning Temporal Dependence from Time-Series Data with Latent Variables

no code implementations27 Aug 2016 Hossein Hosseini, Sreeram Kannan, Baosen Zhang, Radha Poovendran

We consider the setting where a collection of time series, modeled as random processes, evolve in a causal manner, and one is interested in learning the graph governing the relationships of these processes.

Time Series

Activity Recognition Using A Combination of Category Components And Local Models for Video Surveillance

no code implementations28 Feb 2015 Weiyao Lin, Ming-Ting Sun, Radha Poovendran, Zhengyou Zhang

This paper presents a novel approach for automatic recognition of human activities for video surveillance applications.

Activity Recognition

Group Event Detection with a Varying Number of Group Members for Video Surveillance

no code implementations28 Feb 2015 Weiyao Lin, Ming-Ting Sun, Radha Poovendran, Zhengyou Zhang

This paper presents a novel approach for automatic recognition of group activities for video surveillance applications.

Action Detection Activity Detection +1

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