# Adversarial attacks on hybrid classical-quantum Deep Learning models for Histopathological Cancer Detection

arXiv: https://arxiv.org/abs/2309.06377 · HTML: https://mlsanity.com/paper/2309.06377/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 21 of its 35 references that are in the map.
- First posted on arXiv 8 Sep 2023.

## Builds on (most cited first, 21 of 21)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Explaining and Harnessing Adversarial Examples](https://mlsanity.com/paper/1412.6572/) (2014)
- [Towards Deep Learning Models Resistant to Adversarial Attacks](https://mlsanity.com/paper/1706.06083/) (2017)
- [Rethinking the Inception Architecture for Computer Vision](https://mlsanity.com/paper/1512.00567/) (2015)
- [Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attacks](https://mlsanity.com/paper/2003.01690/) (2020)
- [DeepFool: a simple and accurate method to fool deep neural networks](https://mlsanity.com/paper/1511.04599/) (2015)
- [ZOO: Zeroth Order Optimization based Black-box Attacks to Deep Neural Networks without Training Substitute Models](https://mlsanity.com/paper/1708.03999/) (2017)
- [Rotation Equivariant CNNs for Digital Pathology](https://mlsanity.com/paper/1806.03962/) (2018)
- [Knockoff Nets: Stealing Functionality of Black-Box Models](https://mlsanity.com/paper/1812.02766/) (2018)
- [High Accuracy and High Fidelity Extraction of Neural Networks](https://mlsanity.com/paper/1909.01838/) (2019)
- [Transfer learning in hybrid classical-quantum neural networks](https://mlsanity.com/paper/1912.08278/) (2019)
- [Wasserstein Adversarial Examples via Projected Sinkhorn Iterations](https://mlsanity.com/paper/1902.07906/) (2019)
- [Copycat CNN: Stealing Knowledge by Persuading Confession with Random Non-Labeled Data](https://mlsanity.com/paper/1806.05476/) (2018)
- [Adversarial Attacks Against Medical Deep Learning Systems](https://mlsanity.com/paper/1804.05296/) (2018)
- [Accurate, reliable and fast robustness evaluation](https://mlsanity.com/paper/1907.01003/) (2019)
- [Breaking certified defenses: Semantic adversarial examples with spoofed robustness certificates](https://mlsanity.com/paper/2003.08937/) (2020)
- [Simple iterative method for generating targeted universal adversarial perturbations](https://mlsanity.com/paper/1911.06502/) (2019)
- [Adversarial Attacks to Machine Learning-Based Smart Healthcare Systems](https://mlsanity.com/paper/2010.03671/) (2020)
- [Addressing Adversarial Machine Learning Attacks in Smart Healthcare Perspectives](https://mlsanity.com/paper/2112.08862/) (2021)

## Abstract (arXiv)

We present an effective application of quantum machine learning in histopathological cancer detection. The study here emphasizes two primary applications of hybrid classical-quantum Deep Learning models. The first application is to build a classification model for histopathological cancer detection using the quantum transfer learning strategy. The second application is to test the performance of this model for various adversarial attacks. Rather than using a single transfer learning model, the hybrid classical-quantum models are tested using multiple transfer learning models, especially ResNet18, VGG-16, Inception-v3, and AlexNet as feature extractors and integrate it with several quantum circuit-based variational quantum circuits (VQC) with high expressibility. As a result, we provide a comparative analysis of classical models and hybrid classical-quantum transfer learning models for histopathological cancer detection under several adversarial attacks. We compared the performance accuracy of the classical model with the hybrid classical-quantum model using pennylane default quantum simulator. We also observed that for histopathological cancer detection under several adversarial attacks, Hybrid Classical-Quantum (HCQ) models provided better accuracy than classical image classification models.