Cybersecurity has become a great concern in many real-world applications involving
adversaries with Machine Learning (ML) algorithms being more widely used. This concern is …

One of the primary challenges of artificial intelligence in modern computing is providing
privacy and security against adversarial opponents. This survey study covers the most …

The work aims to improve model resilience and accuracy in machine learning (ML) by
addressing data poisoning attacks. Data poisoning attacks are a type of adversarial attack …

Deep learning is a form of artificial intelligence (AI) that has seen rapid development and
deployment in computer software as a means to implementing AI functionality with greater …

Machine Learning (ML) systems often involve a re-training process to make better
predictions and classifications. This re-training process creates a loophole and poses a …

Abstract Machine learning (ML) techniques play a crucial role in producing precise
predictions without the use of explicit programming by utilizing representative and unbiased …

With the huge services provided by machine learning systems in our daily life, the attacks on
these services are increasing every day. The attackers are trying to distort the functionality of …

Data poisoning is identified as a security threat for machine learning models. This paper
explores the poisoning attack against the convolutional neural network under black-box …

Poisoning attacks are a category of adversarial machine learning threats in which an
adversary attempts to subvert the outcome of the machine learning systems by injecting …

Deep neural networks are widely used in many walks of life. Techniques such as transfer
learning enable neural networks pre-trained on certain tasks to be retrained for a new duty …