---
title: "Parameter-Efficient Dual-Loss Adaptation with Logit Divergence: A Unified Approach for Adversarial Example Detection and Robust Inference"
authors:
  - "Zirui Fu"
  - "Marco Donato"
doi: "10.24963/ijcai.2026/122"
url: "https://ijcai.org/proceedings/2026/122"
pdf: "https://ijcai.org/proceedings/2026/0122.pdf"
venue: "Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence"
year: 2026
pages: "1088-1096"
track: "Main Track"
keywords:
  - "Machine Learning: Adversarial machine learning"
  - "Computer Vision: Efficiency and Optimization"
  - "Computer Vision: Transfer, low-shot, semi- and un- supervised learning"
  - "Computer Vision: Adversarial learning, adversarial attack and defense methods"
  - "Machine Learning: Multi-task and transfer learning"
---

# Parameter-Efficient Dual-Loss Adaptation with Logit Divergence: A Unified Approach for Adversarial Example Detection and Robust Inference

Zirui Fu, Marco Donato

*Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence*, pages 1088-1096. DOI: [10.24963/ijcai.2026/122](https://doi.org/10.24963/ijcai.2026/122)

## Abstract

We present D3Adapter, a threat-aware framework that unifies adversarial example detection (AED) and robust inference. D3Adapter attaches a small library of lightweight adapters to a frozen ResNet backbone; the adapters are trained under two loss functions (cross-entropy and optimized reverse cross-entropy) and complementary objectives (clean training and adversarial training), yielding intentionally distinct logit behaviors. During inference, D3Adapter quantifies inter-adapter logit divergence to produce an agreement-based threat score without any external detector for AED. The same pass also performs robust inference by outputting prediction from the selected adapter when an adversarial example is detected, therefore unifying detection and defense into single-pass forward computation. We evaluate D3Adapter under transfer-based and adaptive white-box attacks, and we study scalability across datasets with varying numbers of classes, showing that unified detection and robust inference can be achieved with predictable overhead proportional to the number of adapters.