Add to ORKGNowadays, we are more and more reliant on Deep Learning (DL) models and thus it is essential to safeguard the security of these systems. This paper explores the security issues in Deep Learning and analyses, through the use of experiments, the way forward to build more resilient models. Experiments are conducted to identify the strengths and weaknesses of a new approach to improve the robustness of DL models against adversarial attacks. The results show improvements and new ideas that can be used as recommendations for researchers and practitioners to create increasingly better DL algorithms
Contemporary datasets are rapidly growing in size and complexity. This wealth of data is providing a...
The significance of security is often overlooked until a catastrophic event occurs. This holds for t...
Deep learning is a machine learning technique that enables computers to learn directly from images, ...
Deep learning systems are gaining wider adoption due to their remarkable performances in computer vi...
Adversarial robustness studies the worst-case performance of a machine learning model to ensure safe...
Deep Learning (DL) is having a transformational effect in critical areas such as finance, healthcare...
As deep learning (DL) is becoming a key component in many business and safety-critical systems, such...
Benefiting from the advancement of algorithms in massive data and powerful computing resources, deep...
Deep Learning (DL) algorithms based on artificial neural networks have achieved remarkable success a...
With the widespread applications of deep neural networks, the security of deep neural networks has b...
The monumental achievements of deep learning (DL) systems seem to guarantee the absolute superiority...
Machine Learning (ML) models, in particular Deep Neural Networks (DNNs), have been evolving exceedin...
The use of deep learning (DL) models for solving classification and recognition-related problems are...
In the past decade, Deep Neural Networks (DNNs) have demonstrated outstanding performance in various...
In recent past years, Deep Learning presented an excellent performance in different areas like image...
Contemporary datasets are rapidly growing in size and complexity. This wealth of data is providing a...
The significance of security is often overlooked until a catastrophic event occurs. This holds for t...
Deep learning is a machine learning technique that enables computers to learn directly from images, ...
Deep learning systems are gaining wider adoption due to their remarkable performances in computer vi...
Adversarial robustness studies the worst-case performance of a machine learning model to ensure safe...
Deep Learning (DL) is having a transformational effect in critical areas such as finance, healthcare...
As deep learning (DL) is becoming a key component in many business and safety-critical systems, such...
Benefiting from the advancement of algorithms in massive data and powerful computing resources, deep...
Deep Learning (DL) algorithms based on artificial neural networks have achieved remarkable success a...
With the widespread applications of deep neural networks, the security of deep neural networks has b...
The monumental achievements of deep learning (DL) systems seem to guarantee the absolute superiority...
Machine Learning (ML) models, in particular Deep Neural Networks (DNNs), have been evolving exceedin...
The use of deep learning (DL) models for solving classification and recognition-related problems are...
In the past decade, Deep Neural Networks (DNNs) have demonstrated outstanding performance in various...
In recent past years, Deep Learning presented an excellent performance in different areas like image...
Contemporary datasets are rapidly growing in size and complexity. This wealth of data is providing a...
The significance of security is often overlooked until a catastrophic event occurs. This holds for t...
Deep learning is a machine learning technique that enables computers to learn directly from images, ...