Towards Adversarial Machine Learning and Defenses in Sensors Applications

Dear Colleagues,

Machine Learning has seen immense growth in recent decades across a broad spectrum of applications. The emergence and evolution of deep learning have further improved the performance of many systems. The deep learning models’ evolution has a great impact on computer vision systems, medical imaging, speech recognition, robotics, and self-driving cars. The deep learning models provide excellent performance and reliability due to its ability to generalize for many fields and to identify underlying patterns and make future predictions. The main research areas for deep learning in computer vision, due to its excellent performance, is in surveillance and biometric verification systems. The emergence of adversarial machine learning provides a new direction to the machine learning field where limited data is available. The development of the Generative Adversarial Network (GAN) provides artificial data, which is usually difficult to judge even from the human eye. The artificial data generated by GAN is used for both good and bad purposes.

The ability of deep learning models to observe and recognize patterns makes them valuable in the field of computer vision and image processing. However, deep learning models can be fooled by artificial (fake) data. It has been observed that slight modifications in the images can cause deep learning models to make wrong predictions. These wrong classifications or predictions prove expensive, especially for biometric recognition including speech, face, iris, gait recognition, and autonomous vehicles. These issues make deep learning vulnerable to adversarial attacks. Therefore, there is a strong need to design defenses against such adversarial attacks and this area needs more investigation and research.

This Special Issue aims to provide a forum for individuals from academia and industry to present their novel ideas in the field of adversarial machine learning, GANs, adversarial examples, adversarial defenses, image anonymity, and image forensics. The anticipated research should be industry and application-oriented to handle important issues. Original technical research articles including review papers are invited.

Dr. Irfan Mehmood
Guest Editor

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