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An Improved Optimized Model for Invisible Backdoor Attack Creation Using Steganography

Daniyal M. Alghazzawi1, Osama Bassam J. Rabie1, Surbhi Bhatia2, Syed Hamid Hasan1,*
1 Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, 21589, Saudi Arabia
2 Department of Information Systems, College of Computer Sciences and Information Technology, King Faisal University, Saudi Arabia
* Corresponding Author: Syed Hamid Hasan. Email:
(This article belongs to this Special Issue: Innovations in Artificial Intelligence using Data Mining and Big Data)

Computers, Materials & Continua 2022, 72(1), 1173-1193. https://doi.org/10.32604/cmc.2022.022748

Received 17 August 2021; Accepted 14 December 2021; Issue published 24 February 2022

Abstract

The Deep Neural Networks (DNN) training process is widely affected by backdoor attacks. The backdoor attack is excellent at concealing its identity in the DNN by performing well on regular samples and displaying malicious behavior with data poisoning triggers. The state-of-art backdoor attacks mainly follow a certain assumption that the trigger is sample-agnostic and different poisoned samples use the same trigger. To overcome this problem, in this work we are creating a backdoor attack to check their strength to withstand complex defense strategies, and in order to achieve this objective, we are developing an improved Convolutional Neural Network (ICNN) model optimized using a Gradient-based Optimization (GBO)(ICNN-GBO) algorithm. In the ICNN-GBO model, we are injecting the triggers via a steganography and regularization technique. We are generating triggers using a single-pixel, irregular shape, and different sizes. The performance of the proposed methodology is evaluated using different performance metrics such as Attack success rate, stealthiness, pollution index, anomaly index, entropy index, and functionality. When the CNN-GBO model is trained with the poisoned dataset, it will map the malicious code to the target label. The proposed scheme's effectiveness is verified by the experiments conducted on both the benchmark datasets namely CIDAR-10 and MSCELEB 1M dataset. The results demonstrate that the proposed methodology offers significant defense against the conventional backdoor attack detection frameworks such as STRIP and Neutral cleanse.

Keywords

Convolutional neural network; gradient-based optimization; steganography; backdoor attack; and regularization attack

Cite This Article

D. M. Alghazzawi, O. Bassam J. Rabie, S. Bhatia and S. Hamid Hasan, "An improved optimized model for invisible backdoor attack creation using steganography," Computers, Materials & Continua, vol. 72, no.1, pp. 1173–1193, 2022.



This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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