Invisible CMOS Camera Dazzling for Conducting Adversarial Attacks on Deep Neural Networks
Despite the outstanding performance of deep neural networks, they remain vulnerable to adversarial attacks. While digital domain adversarial attacks are well-documented, most physical-world attacks are typically visible to the human eye. Here, we present a novel invisible optical-based physical adve...
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MDPI AG
2025-04-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/25/7/2301 |
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| _version_ | 1850188287452381184 |
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| author | Zvi Stein Adir Hazan Adrian Stern |
| author_facet | Zvi Stein Adir Hazan Adrian Stern |
| author_sort | Zvi Stein |
| collection | DOAJ |
| description | Despite the outstanding performance of deep neural networks, they remain vulnerable to adversarial attacks. While digital domain adversarial attacks are well-documented, most physical-world attacks are typically visible to the human eye. Here, we present a novel invisible optical-based physical adversarial attack via dazzling a CMOS camera. This attack involves using a designed light pulse sequence spatially transformed within the acquired image due to the camera’s shutter mechanism. We provide a detailed analysis of the photopic conditions required to keep the attacking light source invisible to human observers while effectively disrupting the image, thereby deceiving the DNN. The results indicate that the light source duty cycle controls the tradeoff between the attack’s success rate and the degree of concealment needed. |
| format | Article |
| id | doaj-art-cdbba427a1144941bd5a34826ba62b67 |
| institution | OA Journals |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-cdbba427a1144941bd5a34826ba62b672025-08-20T02:15:54ZengMDPI AGSensors1424-82202025-04-01257230110.3390/s25072301Invisible CMOS Camera Dazzling for Conducting Adversarial Attacks on Deep Neural NetworksZvi Stein0Adir Hazan1Adrian Stern2School of Electrical and Computer Engineering, Ben-Gurion University of the Negev, Beer-Sheva 8410501, IsraelSchool of Electrical and Computer Engineering, Ben-Gurion University of the Negev, Beer-Sheva 8410501, IsraelSchool of Electrical and Computer Engineering, Ben-Gurion University of the Negev, Beer-Sheva 8410501, IsraelDespite the outstanding performance of deep neural networks, they remain vulnerable to adversarial attacks. While digital domain adversarial attacks are well-documented, most physical-world attacks are typically visible to the human eye. Here, we present a novel invisible optical-based physical adversarial attack via dazzling a CMOS camera. This attack involves using a designed light pulse sequence spatially transformed within the acquired image due to the camera’s shutter mechanism. We provide a detailed analysis of the photopic conditions required to keep the attacking light source invisible to human observers while effectively disrupting the image, thereby deceiving the DNN. The results indicate that the light source duty cycle controls the tradeoff between the attack’s success rate and the degree of concealment needed.https://www.mdpi.com/1424-8220/25/7/2301adversarial attackPSFrolling shutterCMOS |
| spellingShingle | Zvi Stein Adir Hazan Adrian Stern Invisible CMOS Camera Dazzling for Conducting Adversarial Attacks on Deep Neural Networks Sensors adversarial attack PSF rolling shutter CMOS |
| title | Invisible CMOS Camera Dazzling for Conducting Adversarial Attacks on Deep Neural Networks |
| title_full | Invisible CMOS Camera Dazzling for Conducting Adversarial Attacks on Deep Neural Networks |
| title_fullStr | Invisible CMOS Camera Dazzling for Conducting Adversarial Attacks on Deep Neural Networks |
| title_full_unstemmed | Invisible CMOS Camera Dazzling for Conducting Adversarial Attacks on Deep Neural Networks |
| title_short | Invisible CMOS Camera Dazzling for Conducting Adversarial Attacks on Deep Neural Networks |
| title_sort | invisible cmos camera dazzling for conducting adversarial attacks on deep neural networks |
| topic | adversarial attack PSF rolling shutter CMOS |
| url | https://www.mdpi.com/1424-8220/25/7/2301 |
| work_keys_str_mv | AT zvistein invisiblecmoscameradazzlingforconductingadversarialattacksondeepneuralnetworks AT adirhazan invisiblecmoscameradazzlingforconductingadversarialattacksondeepneuralnetworks AT adrianstern invisiblecmoscameradazzlingforconductingadversarialattacksondeepneuralnetworks |