Earticle

초록

영어

We have experimentally confirmed that image learning model environments are vulnerable to backdoor attacks that cause misclassification through trigger insertion. Backdoor attacks were influenced by the tradeoff between detection evasion and image quality maintenance, as well as attack stability and disturbance characteristics depending on the multi-resolution environment. We designed and experimented with a total of 10 attack techniques—InputAware, Reflection, LIRA, NeuralCleanse, AdaptiveTrigger, and their hybrid attack models—targeted at images with resolutions of 224× 224, 512× 512, and 1024× 1024. In this research, we used performance metrics such as attack success rate (ASR), PSNR, SSIM, and confidence, and compared and analyzed performance differences according to resolution changes. The experimental results showed that AdaptiveTrigger and Hybrid AdaptiveTrigger achieved 100% attack success rate at all resolutions and demonstrated high attack risk. In particular, the Hybrid InputAware model demonstrated the most balanced performance, showing a balance between success rate and stealthiness, as well as strong stability even with resolution changes. Through this study, we have comprehensively analyzed the threat level and evasion capabilities of various backdoor attack techniques, as well as changes in attack characteristics due to resolution changes. We expect that this research will contribute to the design and defense of attack detection systems targeting image learning in the future.

목차

Abstract
1. Introduction
2. Related Research
3. Experiments and Results of IRNLA based Hybrid Attack Model
3.1 Basic Attack Model
3.2 Hybrid Attack Model
4. Experiments and Results
4.1 Test & Evaluation
5. Conclusions
Acknowledgement
References

키워드

저자

• Jin-keun Hong [ Professor, Division of Advanced IT / X-Tec, Baekseok University, Korea ] Corresponding Author

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Internet, Broadcasting and Communication

• 간기

  계간

• pISSN

  2288-4920

• eISSN

  2288-4939

• 수록기간

  2009~2025

• 십진분류

  KDC 326 DDC 380