Dr. Qi Liu Profile

Dr. Qi Liu

at Florida International Univ

SPIE Involvement:

Author

Publications (1)

This will count as one of your downloads.

You will have access to both the presentation and article (if available).

DOWNLOAD NOW

This content is available for download via your institution's subscription. To access this item, please sign in to your personal account.

Email or Username Forgot your username?

Password Forgot your password?

Show

Keep me signed in

No SPIE account? Create an account

Proceedings Article | 3 May 2018 Presentation + Paper

Understanding adversarial attack and defense towards deep compressed neural networks

Qi Liu, Tao Liu, Wujie Wen

Proceedings Volume 10630, 106300Q (2018) https://doi.org/10.1117/12.2305226

KEYWORDS: Defense and security, Quantization, Systems modeling, Performance modeling, Data modeling, Neural networks, Mathematical modeling, Network security, Resistance

Read Abstract +

Modern deep neural networks (DNNs) have been demonstrating a phenomenal success in many exciting appli- cations such as computer vision, speech recognition, and natural language processing, thanks to recent machine learning model innovation and computing hardware advancement. However, recent studies show that state-of- the-art DNNs can be easily fooled by carefully crafted input perturbations that are even imperceptible to human eyes, namely “adversarial examples”, causing the emerging security concerns for DNN based intelligent systems. Moreover, to ease the intensive computation and memory resources requirement imposed by the fast-growing DNN model size, aggressively pruning the redundant model parameters through various hardware-favorable DNN techniques (i.e. hash, deep compression, circulant projection) has become a necessity. This procedure further complicates the security issues of DNN systems. In this paper, we first study the vulnerabilities of hardware-oriented deep compressed DNNs under various adversarial attacks. Then we survey the existing mitigation approaches such as gradient distillation, which is originally tailored to the software-based DNN systems. Inspired by the gradient distillation and weight reshaping, we further develop a near zero-cost but effective gradient silence (GS) method to protect both software and hardware-based DNN systems against adversarial attacks. Compared with defensive distillation, our gradient salience method can achieve better resilience to adversarial attacks without additional training, while still maintaining very high accuracies across small and large DNN models for various image classification benchmarks like MNIST and CIFAR10.

My Library

You currently do not have any folders to save your paper to! Create a new folder below.

Folder Name

Folder Description

View contact details

UPDATE YOUR PROFILE

Is this your profile? Update it now.

Sign into your SPIE.org account

Don’t have a profile and want one?

Create an account on SPIE.org

Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks. You are receiving this notice because your organization may not have SPIE eBooks access.*

*Shibboleth/Open Athens users─please sign in to access your institution's subscriptions.

To obtain this item, you may purchase the complete book in print or electronic format on SPIE.org.

ORGANIZATIONAL
Sign in with credentials provided by your organization.

Organizational Username

Organizational Password

Show/Hide Password

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

;

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members:

Non-members: ADD TO CART

Keywords/Phrases

Search In:

Publication Years