# Protecting JPEG Images Against Adversarial Attacks

arXiv: https://arxiv.org/abs/1803.00940 · HTML: https://mlsanity.com/paper/1803.00940/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Mar 2018.

## Built on by (most cited first, 4 of 4)

- [Relating Adversarially Robust Generalization to Flat Minima](https://mlsanity.com/paper/2104.04448/) (2021)
- [RoVISQ: Reduction of Video Service Quality via Adversarial Attacks on Deep Learning-based Video Compression](https://mlsanity.com/paper/2203.10183/) (2022)
- [Memory Defense: More Robust Classification via a Memory-Masking Autoencoder](https://mlsanity.com/paper/2202.02595/) (2022)
- [Lossy Compression of Noisy Data for Private and Data-Efficient Learning](https://mlsanity.com/paper/2202.02892/) (2022)

## Abstract (arXiv)

As deep neural networks (DNNs) have been integrated into critical systems, several methods to attack these systems have been developed. These adversarial attacks make imperceptible modifications to an image that fool DNN classifiers. We present an adaptive JPEG encoder which defends against many of these attacks. Experimentally, we show that our method produces images with high visual quality while greatly reducing the potency of state-of-the-art attacks. Our algorithm requires only a modest increase in encoding time, produces a compressed image which can be decompressed by an off-the-shelf JPEG decoder, and classified by an unmodified classifier