Image Segmentation Framework for Detecting Adversarial Attacks for Autonomous Driving Cars

The widespread deployment of deep neural networks (DNNs) in critical real-time applications has spurred significant research into their security and robustness. A key vulnerability identified is that DNN decisions can be maliciously altered by introducing carefully crafted noise into the input data,...

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Published in:	Applied sciences Vol. 15; no. 3; p. 1328
Main Authors:	Sattout, Ahmad Fakhr Aldeen, Chehab, Ali, Mohanna, Ammar, Tajeddine, Razane
Format:	Journal Article
Language:	English
Published:	Basel MDPI AG 01.02.2025
Subjects:	adversarial attacks detection Algorithms autonomous driving cars deep learning image and mask segmentation Image processing k-means Neural networks Safety and security measures Support vector machines Germany
ISSN:	2076-3417, 2076-3417
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Abstract	The widespread deployment of deep neural networks (DNNs) in critical real-time applications has spurred significant research into their security and robustness. A key vulnerability identified is that DNN decisions can be maliciously altered by introducing carefully crafted noise into the input data, leading to erroneous predictions. This is known as an adversarial attack. In this paper, we propose a novel detection framework leveraging segmentation masks and image segmentation techniques to identify adversarial attacks on DNNs, particularly in the context of autonomous driving systems. Our defense technique considers two levels of adversarial detection. The first level mainly detects adversarial inputs with large perturbations using the U-net model and one-class support vector machine (SVM). The second level of defense proposes a dynamic segmentation algorithm based on the k-means algorithm and a verifier model that controls the final prediction of the input image. To evaluate our approach, we comprehensively compare our method to the state-of-the-art feature squeeze method under a white-box attack, using eleven distinct adversarial attacks across three benchmark and heterogeneous data sets. The experimental results demonstrate the efficacy of our framework, achieving overall detection rates exceeding 96% across all adversarial techniques and data sets studied. It is worth mentioning that our method enhances the detection rates of FGSM and BIM attacks, reaching average detection rates of 95.65% as opposed to 62.63% in feature squeezing across the three data sets.
AbstractList	The widespread deployment of deep neural networks (DNNs) in critical real-time applications has spurred significant research into their security and robustness. A key vulnerability identified is that DNN decisions can be maliciously altered by introducing carefully crafted noise into the input data, leading to erroneous predictions. This is known as an adversarial attack. In this paper, we propose a novel detection framework leveraging segmentation masks and image segmentation techniques to identify adversarial attacks on DNNs, particularly in the context of autonomous driving systems. Our defense technique considers two levels of adversarial detection. The first level mainly detects adversarial inputs with large perturbations using the U-net model and one-class support vector machine (SVM). The second level of defense proposes a dynamic segmentation algorithm based on the k-means algorithm and a verifier model that controls the final prediction of the input image. To evaluate our approach, we comprehensively compare our method to the state-of-the-art feature squeeze method under a white-box attack, using eleven distinct adversarial attacks across three benchmark and heterogeneous data sets. The experimental results demonstrate the efficacy of our framework, achieving overall detection rates exceeding 96% across all adversarial techniques and data sets studied. It is worth mentioning that our method enhances the detection rates of FGSM and BIM attacks, reaching average detection rates of 95.65% as opposed to 62.63% in feature squeezing across the three data sets.
Audience	Academic
Author	Sattout, Ahmad Fakhr Aldeen Tajeddine, Razane Chehab, Ali Mohanna, Ammar
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Cites_doi	10.1016/j.procs.2015.06.090 10.1016/j.cie.2005.01.009 10.1109/CVPR.2016.282 10.1007/978-3-319-67361-5_40 10.5201/ipol.2011.bcm_nlm 10.1145/3460120.3484766 10.1109/CVPR42600.2020.00108 10.1109/TIV.2020.3027319 10.1109/SP.2017.49 10.1109/TITS.2023.3317372 10.1201/9781351251389-8 10.5244/C.31.11 10.1109/TIV.2022.3185303 10.1109/CVPR.2018.00175 10.1109/LRA.2024.3490377 10.1109/CVPR52729.2023.00297 10.1109/CVPR46437.2021.01443 10.1109/IVS.2019.8814163 10.24963/ijcai.2018/543 10.14722/vehiclesec.2023.23055 10.1109/IJCNN.2011.6033395 10.1109/IV55152.2023.10186386 10.1109/EuroSP.2016.36 10.1177/0278364913491297 10.1126/scirobotics.abm6074 10.1109/CVPR52688.2022.01491 10.1109/TIV.2022.3173448 10.1007/978-3-319-24574-4_28 10.1109/CVPR.2017.243 10.1109/TIV.2023.3337345 10.1016/j.cose.2023.103345 10.1109/ACCESS.2023.3337046 10.1109/CVPR.2019.00935 10.1109/TITS.2023.3327949 10.14722/ndss.2022.24130 10.1007/978-3-319-66787-4_22 10.1109/IC-CGU58078.2024.10530760 10.14722/ndss.2018.23198 10.1109/CVPR42600.2020.01426 10.1109/SP.2016.41 10.1145/1390156.1390294 10.1145/3133956.3134057 10.3390/electronics12040930 10.1109/SPW53761.2021.00041 10.1109/ICASSP.2019.8683541
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References	ref_50 ref_14 ref_13 ref_57 ref_12 ref_11 ref_55 ref_10 ref_54 Tian (ref_2) 2023; 8 ref_53 ref_51 Zhang (ref_7) 2024; 9 ref_19 ref_18 ref_17 ref_16 ref_59 Yang (ref_1) 2023; 8 ref_60 Yufeng (ref_15) 2023; 132 ref_25 ref_69 ref_24 ref_68 ref_23 ref_67 ref_22 ref_21 ref_65 ref_20 Geiger (ref_52) 2013; 32 ref_64 ref_63 Simonyan (ref_29) 2014; 2 ref_62 ref_28 ref_27 ref_26 ref_72 ref_71 ref_70 Petit (ref_58) 2015; 11 ref_36 ref_35 ref_34 ref_33 ref_77 ref_32 ref_76 ref_31 ref_75 ref_30 ref_74 ref_73 Nawaz (ref_4) 2024; 25 ref_39 ref_37 Wang (ref_41) 2023; 11 Nie (ref_42) 2021; 6 Macenski (ref_56) 2022; 7 Dhanachandra (ref_66) 2015; 54 ref_47 ref_46 Shin (ref_61) 2005; 48 ref_45 Mirza (ref_38) 2014; 27 ref_44 ref_43 Wang (ref_5) 2024; 25 Buades (ref_40) 2011; 1 ref_49 Natan (ref_3) 2023; 8 ref_48 ref_9 ref_8 ref_6
References_xml	– ident: ref_9 – volume: 54 start-page: 764 year: 2015 ident: ref_66 article-title: Image Segmentation Using K-means Clustering Algorithm and Subtractive Clustering Algorithm publication-title: Procedia Comput. Sci. doi: 10.1016/j.procs.2015.06.090 – volume: 11 start-page: 995 year: 2015 ident: ref_58 article-title: Remote Attacks on Automated Vehicles Sensors: Experiments on Camera and Lidar publication-title: Black Hat Eur. – volume: 48 start-page: 395 year: 2005 ident: ref_61 article-title: One-class Support Vector Machines—An Application in Machine Fault Detection and Classification publication-title: Comput. Ind. Eng. doi: 10.1016/j.cie.2005.01.009 – ident: ref_74 – ident: ref_32 – ident: ref_55 – ident: ref_26 – ident: ref_30 doi: 10.1109/CVPR.2016.282 – ident: ref_68 – ident: ref_54 doi: 10.1007/978-3-319-67361-5_40 – volume: 27 start-page: 2672 year: 2014 ident: ref_38 article-title: Generative Adversarial Nets publication-title: Proc. Adv. Neural Inf. Process. Syst. – volume: 1 start-page: 208 year: 2011 ident: ref_40 article-title: Non-Local Means Denoising publication-title: Image Process. Line doi: 10.5201/ipol.2011.bcm_nlm – ident: ref_19 doi: 10.1145/3460120.3484766 – ident: ref_39 – ident: ref_18 doi: 10.1109/CVPR42600.2020.00108 – volume: 6 start-page: 310 year: 2021 ident: ref_42 article-title: A Multimodality Fusion Deep Neural Network and Safety Test Strategy for Intelligent Vehicles publication-title: IEEE Trans. Intell. Veh. doi: 10.1109/TIV.2020.3027319 – ident: ref_71 – ident: ref_77 – ident: ref_27 – ident: ref_31 doi: 10.1109/SP.2017.49 – ident: ref_48 – volume: 25 start-page: 1148 year: 2024 ident: ref_5 article-title: Multi-Sensor Fusion Technology for 3D Object Detection in Autonomous Driving: A Review publication-title: IEEE Trans. Intell. Transp. Syst. doi: 10.1109/TITS.2023.3317372 – ident: ref_23 doi: 10.1201/9781351251389-8 – ident: ref_51 doi: 10.5244/C.31.11 – volume: 8 start-page: 557 year: 2023 ident: ref_3 article-title: End-to-End Autonomous Driving with Semantic Depth Cloud Mapping and Multi-Agent publication-title: IEEE Trans. Intell. Veh. doi: 10.1109/TIV.2022.3185303 – ident: ref_47 doi: 10.1109/CVPR.2018.00175 – volume: 9 start-page: 11361 year: 2024 ident: ref_7 article-title: PEP: Policy-Embedded Trajectory Planning for Autonomous Driving publication-title: IEEE Robot. Autom. Lett. doi: 10.1109/LRA.2024.3490377 – ident: ref_65 doi: 10.1109/CVPR52729.2023.00297 – ident: ref_20 doi: 10.1109/CVPR46437.2021.01443 – ident: ref_6 doi: 10.1109/IVS.2019.8814163 – ident: ref_46 doi: 10.24963/ijcai.2018/543 – ident: ref_16 doi: 10.14722/vehiclesec.2023.23055 – ident: ref_69 doi: 10.1109/IJCNN.2011.6033395 – ident: ref_17 – ident: ref_45 – ident: ref_14 doi: 10.1109/IV55152.2023.10186386 – ident: ref_72 – ident: ref_28 doi: 10.1109/EuroSP.2016.36 – volume: 32 start-page: 1231 year: 2013 ident: ref_52 article-title: Vision Meets Robotics: The Kitti Dataset publication-title: Int. J. Robot. Res. doi: 10.1177/0278364913491297 – volume: 7 start-page: eabm6074 year: 2022 ident: ref_56 article-title: Robot Operating System 2: Design, Architecture, and Uses in The Wild publication-title: Sci. Robot. doi: 10.1126/scirobotics.abm6074 – ident: ref_21 doi: 10.1109/CVPR52688.2022.01491 – volume: 8 start-page: 379 year: 2023 ident: ref_2 article-title: Parallel Learning-Based Steering Control for Autonomous Driving publication-title: IEEE Trans. Intell. Veh. doi: 10.1109/TIV.2022.3173448 – ident: ref_53 – ident: ref_60 doi: 10.1007/978-3-319-24574-4_28 – ident: ref_24 – ident: ref_76 doi: 10.1109/CVPR.2017.243 – volume: 8 start-page: 4678 year: 2023 ident: ref_1 article-title: A Trustworthy Internet of Vehicles: The DAO to Safe, Secure, and Collaborative Autonomous Driving publication-title: IEEE Trans. Intell. Veh. doi: 10.1109/TIV.2023.3337345 – volume: 132 start-page: 103345 year: 2023 ident: ref_15 article-title: Light can be dangerous: Stealthy and Effective Physical-world Adversarial Attack by Spot Light publication-title: Comput. Secur. doi: 10.1016/j.cose.2023.103345 – ident: ref_11 – volume: 11 start-page: 137437 year: 2023 ident: ref_41 article-title: A Novel Lane-Change Decision-Making with Long-Time Trajectory Prediction for Autonomous Vehicle publication-title: IEEE Access doi: 10.1109/ACCESS.2023.3337046 – ident: ref_10 doi: 10.1109/CVPR.2019.00935 – volume: 25 start-page: 3228 year: 2024 ident: ref_4 article-title: Robust Cognitive Capability in Autonomous Driving Using Sensor Fusion Techniques: A Survey publication-title: IEEE Trans. Intell. Transp. Syst. doi: 10.1109/TITS.2023.3327949 – ident: ref_67 – ident: ref_37 – ident: ref_63 – ident: ref_73 – ident: ref_44 doi: 10.14722/ndss.2022.24130 – ident: ref_59 doi: 10.1007/978-3-319-66787-4_22 – ident: ref_8 doi: 10.1109/IC-CGU58078.2024.10530760 – ident: ref_75 – ident: ref_25 doi: 10.14722/ndss.2018.23198 – ident: ref_50 – ident: ref_33 – ident: ref_49 doi: 10.1109/CVPR42600.2020.01426 – ident: ref_34 doi: 10.1109/SP.2016.41 – ident: ref_36 doi: 10.1145/1390156.1390294 – ident: ref_12 – ident: ref_35 doi: 10.1145/3133956.3134057 – ident: ref_62 doi: 10.3390/electronics12040930 – ident: ref_13 doi: 10.1109/SPW53761.2021.00041 – ident: ref_70 – ident: ref_43 – ident: ref_22 – ident: ref_64 doi: 10.1109/ICASSP.2019.8683541 – volume: 2 start-page: 2 year: 2014 ident: ref_29 article-title: Visualising Image Classification Models and Saliency Maps publication-title: Deep. Inside Convolutional Netw. – ident: ref_57
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SubjectTerms	adversarial attacks detection Algorithms autonomous driving cars deep learning image and mask segmentation Image processing k-means Neural networks Safety and security measures Support vector machines
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Title	Image Segmentation Framework for Detecting Adversarial Attacks for Autonomous Driving Cars
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