Impact of Architectural Modifications on Deep Learning Adversarial
Robustness
Impact of Architectural Modifications on Deep Learning Adversarial
Robustness
Rapid advancements of deep learning are accelerating adoption in a wide variety of applications, including safety-critical applications such as self-driving vehicles, drones, robots, and surveillance systems. These advancements include applying variations of sophisticated techniques that improve the performance of models. However, such models are not immune to adversarial manipulations, which …