# Energy Backdoor Attack to Deep Neural Networks

arXiv: https://arxiv.org/abs/2501.08152 · HTML: https://mlsanity.com/paper/2501.08152/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 34 references that are in the map.
- First posted on arXiv 14 Jan 2025.

## Builds on (most cited first, 20 of 20)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Going Deeper with Convolutions](https://mlsanity.com/paper/1409.4842/) (2014)
- [MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications](https://mlsanity.com/paper/1704.04861/) (2017)
- [Poison Frogs! Targeted Clean-Label Poisoning Attacks on Neural Networks](https://mlsanity.com/paper/1804.00792/) (2018)
- [Reflection Backdoor: A Natural Backdoor Attack on Deep Neural Networks](https://mlsanity.com/paper/2007.02343/) (2020)
- [A new Backdoor Attack in CNNs by training set corruption without label poisoning](https://mlsanity.com/paper/1902.11237/) (2019)
- [Learning to Optimize](https://mlsanity.com/paper/1606.01885/) (2016)
- [SCNN: An Accelerator for Compressed-sparse Convolutional Neural Networks](https://mlsanity.com/paper/1708.04485/) (2017)
- [Sponge Examples: Energy-Latency Attacks on Neural Networks](https://mlsanity.com/paper/2006.03463/) (2020)
- [NICGSlowDown: Evaluating the Efficiency Robustness of Neural Image Caption Generation Models](https://mlsanity.com/paper/2203.15859/) (2022)
- [An Overview of Backdoor Attacks Against Deep Neural Networks and Possible Defences](https://mlsanity.com/paper/2111.08429/) (2021)
- [Backdoor Attacks Against Deep Image Compression via Adaptive Frequency Trigger](https://mlsanity.com/paper/2302.14677/) (2023)
- [Hardware Implementation of Deep Network Accelerators Towards Healthcare and Biomedical Applications](https://mlsanity.com/paper/2007.05657/) (2020)
- [Energy-Latency Attacks via Sponge Poisoning](https://mlsanity.com/paper/2203.08147/) (2022)
- [Embedded Encoder-Decoder in Convolutional Networks Towards Explainable AI](https://mlsanity.com/paper/2007.06712/) (2020)
- [Backdoor Attack with Sparse and Invisible Trigger](https://mlsanity.com/paper/2306.06209/) (2023)
- [Energy-Latency Attacks to On-Device Neural Networks via Sponge Poisoning](https://mlsanity.com/paper/2305.03888/) (2023)
- [The Impact of Uniform Inputs on Activation Sparsity and Energy-Latency Attacks in Computer Vision](https://mlsanity.com/paper/2403.18587/) (2024)
- [EREBA: Black-box Energy Testing of Adaptive Neural Networks](https://mlsanity.com/paper/2202.06084/) (2022)
- [SlowFormer: Universal Adversarial Patch for Attack on Compute and Energy Efficiency of Inference Efficient Vision Transformers](https://mlsanity.com/paper/2310.02544/) (2023)

## Built on by (most cited first, 1 of 1)

- [Energy-Latency Attacks: A New Adversarial Threat to Deep Learning](https://mlsanity.com/paper/2503.04963/) (2025)

## Abstract (arXiv)

The rise of deep learning (DL) has increased computing complexity and energy use, prompting the adoption of application specific integrated circuits (ASICs) for energy-efficient edge and mobile deployment. However, recent studies have demonstrated the vulnerability of these accelerators to energy attacks. Despite the development of various inference time energy attacks in prior research, backdoor energy attacks remain unexplored. In this paper, we design an innovative energy backdoor attack against deep neural networks (DNNs) operating on sparsity-based accelerators. Our attack is carried out in two distinct phases: backdoor injection and backdoor stealthiness. Experimental results using ResNet-18 and MobileNet-V2 models trained on CIFAR-10 and Tiny ImageNet datasets show the effectiveness of our proposed attack in increasing energy consumption on trigger samples while preserving the model's performance for clean/regular inputs. This demonstrates the vulnerability of DNNs to energy backdoor attacks. The source code of our attack is available at: https://github.com/hbrachemi/energy_backdoor.