# LESSON: Multi-Label Adversarial False Data Injection Attack for Deep Learning Locational Detection

arXiv: https://arxiv.org/abs/2401.16001 · HTML: https://mlsanity.com/paper/2401.16001/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Jan 2024.

## Built on by (most cited first, 8 of 8)

- [Advances and Open Challenges in Federated Foundation Models](https://mlsanity.com/paper/2404.15381/) (2024)
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- [Image Captioning in news report scenario](https://mlsanity.com/paper/2403.16209/) (2024)
- [Comprehensive evaluation of Mal-API-2019 dataset by machine learning in malware detection](https://mlsanity.com/paper/2403.02232/) (2024)
- [Advanced Feature Manipulation for Enhanced Change Detection Leveraging Natural Language Models](https://mlsanity.com/paper/2403.15943/) (2024)
- [A transfer learning approach for automatic conflicts detection in software requirement sentence pairs based on dual encoders](https://mlsanity.com/paper/2511.23007/) (2025)
- [Advantage-based Temporal Attack in Reinforcement Learning](https://mlsanity.com/paper/2602.19582/) (2026)

## Abstract (arXiv)

Deep learning methods can not only detect false data injection attacks (FDIA) but also locate attacks of FDIA. Although adversarial false data injection attacks (AFDIA) based on deep learning vulnerabilities have been studied in the field of single-label FDIA detection, the adversarial attack and defense against multi-label FDIA locational detection are still not involved. To bridge this gap, this paper first explores the multi-label adversarial example attacks against multi-label FDIA locational detectors and proposes a general multi-label adversarial attack framework, namely muLti-labEl adverSarial falSe data injectiON attack (LESSON). The proposed LESSON attack framework includes three key designs, namely Perturbing State Variables, Tailored Loss Function Design, and Change of Variables, which can help find suitable multi-label adversarial perturbations within the physical constraints to circumvent both Bad Data Detection (BDD) and Neural Attack Location (NAL). Four typical LESSON attacks based on the proposed framework and two dimensions of attack objectives are examined, and the experimental results demonstrate the effectiveness of the proposed attack framework, posing serious and pressing security concerns in smart grids.