Random Logit Scaling: Defending Deep Neural Networks Against Black-Box Score-Based Adversarial Example Attacks

2026-07-16 · Source: Artificial Intelligence · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Cybersecurity & Data Privacy · Depth: Expert, quick

Summary

Random Logit Scaling (RLS) is a newly proposed randomization-based defense designed to protect deep neural networks against black-box score-based adversarial example attacks. This plug-and-play, post-processing defense can be implemented on any existing machine learning model with minimal effort. RLS operates by confusing attackers through the output of falsified scores, which are generated from randomly scaled logits, all while preserving the model's original accuracy. The defense significantly reduces the success rate of leading black-box score-based attacks and minimizes confidence score distortion when compared to other prominent randomization-based defenses. Additionally, the research introduces a novel adaptive attack that exposes vulnerabilities in AAA, a prominent non-randomized black-box defense that also modifies output logits to deter attackers.

Key takeaway

For AI Security Engineers deploying machine learning models in environments susceptible to black-box adversarial attacks, Random Logit Scaling (RLS) provides a compelling, low-cost defense. You should consider integrating this plug-and-play post-processing technique to significantly reduce attack success rates without compromising model accuracy. Evaluate RLS's effectiveness against known leading score-based attacks and be mindful of potential adaptive attack developments, as demonstrated by the vulnerability of other logit-modifying defenses.

Key insights

RLS defends against black-box adversarial attacks by randomly scaling logits, maintaining accuracy while confusing attackers.

Principles

• Randomization can confuse black-box attackers.
• Post-processing defenses offer plug-and-play integration.
• Maintaining accuracy is crucial for defense adoption.

Method

RLS applies random scaling to output logits, generating falsified scores to mislead black-box score-based attackers while preserving the model's original classification accuracy.

In practice

• Implement RLS as a post-processing layer.
• Test RLS against leading black-box attacks.
• Evaluate defense against adaptive attacks.

Topics

• Random Logit Scaling
• Adversarial Attacks
• Black-Box Security
• Deep Learning Defense
• Machine Learning Security

Best for: AI Engineer, Research Scientist, AI Scientist, Machine Learning Engineer, AI Security Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Artificial Intelligence.