# PoTrojan: powerful neural-level trojan designs in deep learning models

arXiv: https://arxiv.org/abs/1802.03043 · HTML: https://mlsanity.com/paper/1802.03043/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Feb 2018.

## Built on by (most cited first, 10 of 10)

- [Robust Machine Learning Systems: Challenges, Current Trends, Perspectives, and the Road Ahead](https://mlsanity.com/paper/2101.02559/) (2021)
- [Robust Backdoor Attacks against Deep Neural Networks in Real Physical World](https://mlsanity.com/paper/2104.07395/) (2021)
- [SoWaF: Shuffling of Weights and Feature Maps: A Novel Hardware Intrinsic Attack (HIA) on Convolutional Neural Network (CNN)](https://mlsanity.com/paper/2103.09327/) (2021)
- [Performance-lossless Black-box Model Watermarking](https://mlsanity.com/paper/2312.06488/) (2023)
- [FeSHI: Feature Map Based Stealthy Hardware Intrinsic Attack](https://mlsanity.com/paper/2106.06895/) (2021)
- [SanitAIs: Unsupervised Data Augmentation to Sanitize Trojaned Neural Networks](https://mlsanity.com/paper/2109.04566/) (2021)
- [Identifying Backdoor Attacks in Federated Learning via Anomaly Detection](https://mlsanity.com/paper/2202.04311/) (2022)
- [DeepBaR: Fault Backdoor Attack on Deep Neural Network Layers](https://mlsanity.com/paper/2407.21220/) (2024)
- [Privacy protection based on mask template](https://mlsanity.com/paper/2202.06250/) (2022)
- [A Survey of Trojan Attacks and Defenses to Deep Neural Networks](https://mlsanity.com/paper/2408.08920/) (2024)

## Abstract (arXiv)

With the popularity of deep learning (DL), artificial intelligence (AI) has been applied in many areas of human life. Neural network or artificial neural network (NN), the main technique behind DL, has been extensively studied to facilitate computer vision and natural language recognition. However, the more we rely on information technology, the more vulnerable we are. That is, malicious NNs could bring huge threat in the so-called coming AI era. In this paper, for the first time in the literature, we propose a novel approach to design and insert powerful neural-level trojans or PoTrojan in pre-trained NN models. Most of the time, PoTrojans remain inactive, not affecting the normal functions of their host NN models. PoTrojans could only be triggered in very rare conditions. Once activated, however, the PoTrojans could cause the host NN models to malfunction, either falsely predicting or classifying, which is a significant threat to human society of the AI era. We would explain the principles of PoTrojans and the easiness of designing and inserting them in pre-trained deep learning models. PoTrojans doesn't modify the existing architecture or parameters of the pre-trained models, without re-training. Hence, the proposed method is very efficient.