401 citations · 529 across the 35 of their papers we have counts for
13 papers · 1 filter
Predictive Entropy as a Joint Screen for Error and Paraphrase Instability in Medical Vision-Language Models
Binesh Sadanandan, Vahid Behzadan
Medical Vision-Language Models (VLMs) answering binary presence questions on chest radiographs can fail in two linked ways: they are confidently wrong, and they change answers when…
Mitigation of Adversarial Policy Imitation via Constrained Randomization of Policy (CRoP)
Nancirose Piazza, Vahid Behzadan
Deep reinforcement learning (DRL) policies are vulnerable to unauthorized replication attacks, where an adversary exploits imitation learning to reproduce target policies from obse…
Adversarial Poisoning Attacks and Defense for General Multi-Class Models Based On Synthetic Reduced Nearest Neighbors
Pooya Tavallali, Vahid Behzadan, Peyman Tavallali +1
State-of-the-art machine learning models are vulnerable to data poisoning attacks whose purpose is to undermine the integrity of the model. However, the current literature on data…
Adversarial Attacks on Deep Algorithmic Trading Policies
Yaser Faghan, Nancirose Piazza, Vahid Behzadan +1
Deep Reinforcement Learning (DRL) has become an appealing solution to algorithmic trading such as high frequency trading of stocks and cyptocurrencies. However, DRL have been shown…
Sequential Triggers for Watermarking of Deep Reinforcement Learning Policies
Vahid Behzadan, William Hsu
This paper proposes a novel scheme for the watermarking of Deep Reinforcement Learning (DRL) policies. This scheme provides a mechanism for the integration of a unique identifier w…
Adversarial Exploitation of Policy Imitation
Vahid Behzadan, William Hsu
This paper investigates a class of attacks targeting the confidentiality aspect of security in Deep Reinforcement Learning (DRL) policies. Recent research have established the vuln…
Analysis and Improvement of Adversarial Training in DQN Agents With Adversarially-Guided Exploration (AGE)
Vahid Behzadan, William Hsu
This paper investigates the effectiveness of adversarial training in enhancing the robustness of Deep Q-Network (DQN) policies to state-space perturbations. We first present a form…
RL-Based Method for Benchmarking the Adversarial Resilience and Robustness of Deep Reinforcement Learning Policies
Vahid Behzadan, William Hsu
This paper investigates the resilience and robustness of Deep Reinforcement Learning (DRL) policies to adversarial perturbations in the state space. We first present an approach fo…
Emergence of Addictive Behaviors in Reinforcement Learning Agents
Vahid Behzadan, Roman V. Yampolskiy, Arslan Munir
This paper presents a novel approach to the technical analysis of wireheading in intelligent agents. Inspired by the natural analogues of wireheading and their prevalent manifestat…
The Faults in Our Pi Stars: Security Issues and Open Challenges in Deep Reinforcement Learning
Vahid Behzadan, Arslan Munir
Since the inception of Deep Reinforcement Learning (DRL) algorithms, there has been a growing interest in both research and industrial communities in the promising potentials of th…
Mitigation of Policy Manipulation Attacks on Deep Q-Networks with Parameter-Space Noise
Vahid Behzadan, Arslan Munir
Recent developments have established the vulnerability of deep reinforcement learning to policy manipulation attacks via intentionally perturbed inputs, known as adversarial exampl…
Vulnerability of Deep Reinforcement Learning to Policy Induction Attacks
Vahid Behzadan, Arslan Munir
Deep learning classifiers are known to be inherently vulnerable to manipulation by intentionally perturbed inputs, named adversarial examples. In this work, we establish that reinf…
Technical Report on the CleverHans v2.1.0 Adversarial Examples Library
Nicolas Papernot, Fartash Faghri, Nicholas Carlini +23
CleverHans is a software library that provides standardized reference implementations of adversarial example construction techniques and adversarial training. The library may be us…