# Secure Deep Learning Engineering: A Software Quality Assurance Perspective

arXiv: https://arxiv.org/abs/1810.04538 · HTML: https://mlsanity.com/paper/1810.04538/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Oct 2018.

## Built on by (most cited first, 3 of 3)

- [ArchRepair: Block-Level Architecture-Oriented Repairing for Deep Neural Networks](https://mlsanity.com/paper/2111.13330/) (2021)
- [Characterizing and Understanding the Behavior of Quantized Models for Reliable Deployment](https://mlsanity.com/paper/2204.04220/) (2022)
- [Image classifiers can not be made robust to small perturbations](https://mlsanity.com/paper/2112.04033/) (2021)

## Abstract (arXiv)

Over the past decades, deep learning (DL) systems have achieved tremendous success and gained great popularity in various applications, such as intelligent machines, image processing, speech processing, and medical diagnostics. Deep neural networks are the key driving force behind its recent success, but still seem to be a magic black box lacking interpretability and understanding. This brings up many open safety and security issues with enormous and urgent demands on rigorous methodologies and engineering practice for quality enhancement. A plethora of studies have shown that the state-of-the-art DL systems suffer from defects and vulnerabilities that can lead to severe loss and tragedies, especially when applied to real-world safety-critical applications. In this paper, we perform a large-scale study and construct a paper repository of 223 relevant works to the quality assurance, security, and interpretation of deep learning. We, from a software quality assurance perspective, pinpoint challenges and future opportunities towards universal secure deep learning engineering. We hope this work and the accompanied paper repository can pave the path for the software engineering community towards addressing the pressing industrial demand of secure intelligent applications.