# Detection based Defense against Adversarial Examples from the Steganalysis Point of View

arXiv: https://arxiv.org/abs/1806.09186 · HTML: https://mlsanity.com/paper/1806.09186/

## Key facts

- Cited by 32 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Jun 2018.

## Built on by (most cited first, 30 of 32)

- [Advances in adversarial attacks and defenses in computer vision: A survey](https://mlsanity.com/paper/2108.00401/) (2021)
- [Evading Adversarial Example Detection Defenses with Orthogonal Projected Gradient Descent](https://mlsanity.com/paper/2106.15023/) (2021)
- [SpectralDefense: Detecting Adversarial Attacks on CNNs in the Fourier Domain](https://mlsanity.com/paper/2103.03000/) (2021)
- [Detecting Adversarial Examples from Sensitivity Inconsistency of Spatial-Transform Domain](https://mlsanity.com/paper/2103.04302/) (2021)
- [Attack to Fool and Explain Deep Networks](https://mlsanity.com/paper/2106.10606/) (2021)
- [Deep Learning for Steganalysis of Diverse Data Types: A review of methods, taxonomy, challenges and future directions](https://mlsanity.com/paper/2308.04522/) (2023)
- [Detecting Adversarial Perturbations in Multi-Task Perception](https://mlsanity.com/paper/2203.01177/) (2022)
- [Exploiting Multi-Object Relationships for Detecting Adversarial Attacks in Complex Scenes](https://mlsanity.com/paper/2108.08421/) (2021)
- [Multi-Expert Adversarial Attack Detection in Person Re-identification Using Context Inconsistency](https://mlsanity.com/paper/2108.09891/) (2021)
- [What You See is Not What the Network Infers: Detecting Adversarial Examples Based on Semantic Contradiction](https://mlsanity.com/paper/2201.09650/) (2022)
- [Detecting AutoAttack Perturbations in the Frequency Domain](https://mlsanity.com/paper/2111.08785/) (2021)
- [NoiLIn: Improving Adversarial Training and Correcting Stereotype of Noisy Labels](https://mlsanity.com/paper/2105.14676/) (2021)
- [TextShield: Beyond Successfully Detecting Adversarial Sentences in Text Classification](https://mlsanity.com/paper/2302.02023/) (2023)
- [Self-recoverable Adversarial Examples: A New Effective Protection Mechanism in Social Networks](https://mlsanity.com/paper/2204.12050/) (2022)
- [Adversarial Training Makes Weight Loss Landscape Sharper in Logistic Regression](https://mlsanity.com/paper/2102.02950/) (2021)
- [Adversarial Examples Detection beyond Image Space](https://mlsanity.com/paper/2102.11586/) (2021)
- [Be Your Own Neighborhood: Detecting Adversarial Example by the Neighborhood Relations Built on Self-Supervised Learning](https://mlsanity.com/paper/2209.00005/) (2022)
- [Adversarial Feature Alignment: Balancing Robustness and Accuracy in Deep Learning via Adversarial Training](https://mlsanity.com/paper/2402.12187/) (2024)
- [Unveiling AI's Blind Spots: An Oracle for In-Domain, Out-of-Domain, and Adversarial Errors](https://mlsanity.com/paper/2410.02384/) (2024)
- [Adaptive-Gravity: A Defense Against Adversarial Samples](https://mlsanity.com/paper/2204.03694/) (2022)
- [Reversing Skin Cancer Adversarial Examples by Multiscale Diffusive and Denoising Aggregation Mechanism](https://mlsanity.com/paper/2208.10373/) (2022)
- [Adversarial Examples Detection with Enhanced Image Difference Features based on Local Histogram Equalization](https://mlsanity.com/paper/2305.04436/) (2023)
- [Spatial-Frequency Discriminability for Revealing Adversarial Perturbations](https://mlsanity.com/paper/2305.10856/) (2023)
- [Hierarchical Invariance for Robust and Interpretable Vision Tasks at Larger Scales](https://mlsanity.com/paper/2402.15430/) (2024)
- [A Few Large Shifts: Layer-Inconsistency Based Minimal Overhead Adversarial Example Detection](https://mlsanity.com/paper/2505.12586/) (2025)
- [MixDefense: A Defense-in-Depth Framework for Adversarial Example Detection Based on Statistical and Semantic Analysis](https://mlsanity.com/paper/2104.10076/) (2021)
- [Repairing Adversarial Texts through Perturbation](https://mlsanity.com/paper/2201.02504/) (2021)
- [3D-VFD: A Victim-free Detector against 3D Adversarial Point Clouds](https://mlsanity.com/paper/2205.08738/) (2022)
- [AED-PADA:Improving Generalizability of Adversarial Example Detection via Principal Adversarial Domain Adaptation](https://mlsanity.com/paper/2404.12635/) (2024)
- [Contract And Conquer: How to Provably Compute Adversarial Examples for a Black-Box Model?](https://mlsanity.com/paper/2603.10689/) (2026)

## Abstract (arXiv)

Deep Neural Networks (DNNs) have recently led to significant improvements in many fields. However, DNNs are vulnerable to adversarial examples which are samples with imperceptible perturbations while dramatically misleading the DNNs. Moreover, adversarial examples can be used to perform an attack on various kinds of DNN based systems, even if the adversary has no access to the underlying model. Many defense methods have been proposed, such as obfuscating gradients of the networks or detecting adversarial examples. However it is proved out that these defense methods are not effective or cannot resist secondary adversarial attacks. In this paper, we point out that steganalysis can be applied to adversarial examples detection, and propose a method to enhance steganalysis features by estimating the probability of modifications caused by adversarial attacks. Experimental results show that the proposed method can accurately detect adversarial examples. Moreover, secondary adversarial attacks cannot be directly performed to our method because our method is not based on a neural network but based on high-dimensional artificial features and FLD (Fisher Linear Discriminant) ensemble.