events in atlanta Adversarial attacks on deep learning models: Konda Reddy Mopuri

Recent researh efforts show that deep learning models are vulnerable to small but structured perturbations. This quasi-imperceptible noise can fool the state-of-the-art deep models (eg: object recognition CNNs) to infer wrong predictions. This noise is referred to as adversarial perturbation. It is shown that perturbation computed for one network is able to fool a new network trained with different architecture or subset of training data. Transferability of these adversarial perturbations across multiple networks is a serious issue for deploying the models. In my talk, I would introduce the concept of adversarial images/attacks and discuss recent developments in this area. Takeaways from my talk would be, (i) To understand a fundamental problem (existance adversarial perturbations) that exists with today’s deep learning, (ii) Some of the existing approaches to craft the adversarial attacks and (iii) Most efficient techniques to date for defending deep learned models against them. This would be very relevant to everybody who wants to train or deploy a deep network (for eg: object recognition, semantic segmentation, etc).

Slides and more info: https://anthillinside.talkfunnel.com/...

events in atlanta Adversarial attacks on deep learning models: Konda Reddy Mopuri
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