### Adversarial Attacks against Deep Learning Models

Research Question: Is Deep Learning secure for Robots?

• Background

Han Wu    @wuhanstudio

Ph.D. Student at the University of Exeter, in the U.K.

## Background

Is Deep Learning secure for Robots?

Intelligent Robots: Deep Learning in Robotics

Deep Learning for Autonomous Driving

Deep Learning Models are differentiable

Deep Learning Models are differentiable

Deep Learning Models are differentiable

One Targeted Attack

Multi Targeted Attack

Multi Untargeted Attack

## Adversarial Detection: One Targeted Attack

$$J_1(x, \delta, y_h) = max(\ \sigma(c) * \sigma(p_0) \ )$$

                        
# One Targeted Attack
loss = K.max(K.sigmoid(K.reshape(out, (-1, 8))[:, 4]) * K.sigmoid(K.reshape(out, (-1, 8))[:, 5]))




## Adversarial Detection: Multi Targeted Attack

$$J_1(x, \delta, y_h) = max(\ \sigma(c) * \sigma(p_0) \ )$$

$$J_2(x, \delta, y_h) = \sigma(c) * \sigma(p_0)$$

                        
# Multi Targeted Attack
loss = K.sigmoid(K.reshape(out, (-1, 8))[:, 4]) * K.sigmoid(K.reshape(out, (-1, 8))[:, 5])




## Adversarial Detection: Multi Untargeted Attack

$$J_1(x, \delta, y_h) = max(\ \sigma(c) * \sigma(p_0) \ )$$

$$J_2(x, \delta, y_h) = \sigma(c) * \sigma(p_0)$$

$$J_3(x, \delta, y_h) = \sigma(c) * \sum\sigma(p_i)$$

                        
# Multi Untargeted Attack
loss = K.sigmoid(K.reshape(out, (-1, 8))[:, 4]) * K.sum(K.sigmoid(K.reshape(out, (-1, 8))[:, 5:]))