# Code-Bridged Classifier (CBC): A Low or Negative Overhead Defense for Making a CNN Classifier Robust Against Adversarial Attacks

arXiv: https://arxiv.org/abs/2001.06099 · HTML: https://mlsanity.com/paper/2001.06099/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Jan 2020.

## Built on by (most cited first, 2 of 2)

- [Online User Profiling to Detect Social Bots on Twitter](https://mlsanity.com/paper/2203.05966/) (2022)
- [Adaptive-Gravity: A Defense Against Adversarial Samples](https://mlsanity.com/paper/2204.03694/) (2022)

## Abstract (arXiv)

In this paper, we propose Code-Bridged Classifier (CBC), a framework for making a Convolutional Neural Network (CNNs) robust against adversarial attacks without increasing or even by decreasing the overall models' computational complexity. More specifically, we propose a stacked encoder-convolutional model, in which the input image is first encoded by the encoder module of a denoising auto-encoder, and then the resulting latent representation (without being decoded) is fed to a reduced complexity CNN for image classification. We illustrate that this network not only is more robust to adversarial examples but also has a significantly lower computational complexity when compared to the prior art defenses.