What are the implications of Curriculum Learning strategy on IRL methods?: Investigating Inverse Reinforcement Learning from Human Behavior

Inverse Reinforcement Learning (IRL) is a subfield of Reinforcement Learning (RL) that focuses on recovering the reward function using expert demonstrations. In the field of IRL, Adversarial IRL (AIRL) is a promising algorithm that is postulated to recover non-linear rewards in environments with unknown dynamics. This study investigates the...

Adversarial Attack and Training on Deep Learning-based Gaze estimation

Recently, while gaze estimation has gained a substantial improvement by using deep learning models, research had shown that neural networks are weak against adversarial attacks. Despite researchers has been done numerous on adversarial training, there are little to no studies on adversarial training in gaze estimation. Therefore, the objective...

Employing Deep Ensemble Learning for Improving the Security of Computer Networks against Adversarial Attacks

In the past few years, Convolutional Neural Networks (CNN) have demonstrated promising performance in various real-world cybersecurity applications, such as network and multimedia security. However, the underlying fragility of CNN structures poses major security problems, making them inappropriate for use in security-oriented applications,...

Improving the robustness of decision trees in security-sensitive setting

Machine learning is used for security purposes, to differ between the benign and the malicious. Where decision trees can lead to understandable and explainable classifications, an adversary could manipulate the model input to evade detection, e.g. the malicious been classified as the benign. State-of-the-art techniques improve the robustness by...

Adversarially Robust Decision Trees Against User-Specified Threat Models

In the present day we use machine learning for sensitive tasks that require models to be both understandable and robust. Although traditional models such as decision trees are understandable, they suffer from adversarial attacks. When a decision tree is used to differentiate between a user's benign and malicious behavior, an adversarial attack...

Constrained Concealment Attacks against Reconstruction-based Anomaly Detectors in Industrial Control Systems

Recently, reconstruction-based anomaly detection was proposed as an effective technique to detect attacks in dynamic industrial control networks. Unlike classical network anomaly detectors that observe the network traffic, reconstruction-based detectors operate on the measured sensor data, leveraging physical process models learned a priori....

The impact of image filters on machine classification and human perception of weather conditions

Nowadays with the growth of social media, users upload millions of photos in different platforms online. Researchers in the field of computer vision devote their time and effort to analyze images in order to gain valuable insight. Data<br/>analysis and classification can be impeded by different factors. One of which is the image filters that are...

PRECLUDE: PRivacy-prEserving Collaborative Learning Using a Decentralised Ensemble approach

Machine learning techniques receive significant responsibilities, despite growing privacy concerns. Early-stage autonomous vehicles are increasingly appearing on the streets, carrying the burden of transporting human-lives to their destination. Meanwhile, doctors are involving Artificial Intelligence (AI) in their medical diagnoses, basing...