Strategies to architect AI Safety: Defense to guard AI from Adversaries

TL;DR

This paper proposes a comprehensive strategy and a novel architecture, Dynamic Neural Defense (DND), to enhance AI security against adversarial attacks through randomness, detection, and visual similarity exploitation.

Contribution

The paper introduces DND, a new deep learning architecture combining randomness, sequence analysis, and visual similarity to defend AI from adversarial threats.

Findings

DND evades exploratory attacks by hiding learning processes.

DND detects adversarial sequences using LSTM analysis.

DND prevents hacking by using visual similarity inputs.

Abstract

The impact of designing for security of AI is critical for humanity in the AI era. With humans increasingly becoming dependent upon AI, there is a need for neural networks that work reliably, inspite of Adversarial attacks. The vision for Safe and secure AI for popular use is achievable. To achieve safety of AI, this paper explores strategies and a novel deep learning architecture. To guard AI from adversaries, paper explores combination of 3 strategies: 1. Introduce randomness at inference time to hide the representation learning from adversaries. 2. Detect presence of adversaries by analyzing the sequence of inferences. 3. Exploit visual similarity. To realize these strategies, this paper designs a novel architecture, Dynamic Neural Defense, DND. This defense has 3 deep learning architectural features: 1. By hiding the way a neural network learns from exploratory attacks using a random computation graph, DND evades attack. 2. By analyzing input sequence to cloud AI inference engine with LSTM, DND detects attack sequence. 3. By inferring with visual similar inputs generated by VAE, any AI defended by DND approach does not succumb to hackers. Thus, a roadmap to develop reliable, safe and secure AI is presented.