Object Distance Estimation Using a Single Image Taken from a Moving Rolling Shutter Camera

Kim, Namhoon; Bae, Jin Ho; Kim, Cheolhwan; Park, Soyeon; Sohn, Hong Gyoo

doi:10.3390/s20143860

Cited by 9 publications

(4 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Invisible perturbation: By exploiting the unique characteristics of cameras, camera-based approaches have demonstrated successful adversarial attacks in the physical domain. Sayles et al [50] made a significant discovery regarding the rolling shutter effect commonly found in consumer-grade cameras, including cell phones [145]. They found that this effect can generate precise striping patterns that manifest in captured images.…”

Section: Imaging Device Manipulation-based Attackmentioning

confidence: 99%

Physical Adversarial Attacks for Camera-Based Smart Systems: Current Trends, Categorization, Applications, Research Challenges, and Future Outlook

Guesmi,

Hanif,

Ouni

et al. 2023

IEEE Access

View full text Add to dashboard Cite

Deep Neural Networks (DNNs) have shown impressive performance in computer vision tasks; however, their vulnerability to adversarial attacks raises concerns regarding their security and reliability. Extensive research has shown that DNNs can be compromised by carefully crafted perturbations, leading to significant performance degradation in both digital and physical domains. Therefore, ensuring the security of DNN-based systems is crucial, particularly in safety-critical domains such as autonomous driving, robotics, smart homes/cities, smart industries, video surveillance, and healthcare. In this paper, we present a comprehensive survey of the current trends focusing specifically on physical adversarial attacks. We aim to provide a thorough understanding of the concept of physical adversarial attacks, analyzing their key characteristics and distinguishing features. Furthermore, we explore the specific requirements and challenges associated with executing attacks in the physical world. Our article delves into various physical adversarial attack methods, categorized according to their target tasks in different applications, including classification, detection, face recognition, semantic segmentation and depth estimation. We assess the performance of these attack methods in terms of their effectiveness, stealthiness, and robustness. We examine how each technique strives to ensure the successful manipulation of DNNs while mitigating the risk of detection and withstanding real-world distortions. Lastly, we discuss the current challenges and outline potential future research directions in the field of physical adversarial attacks. We highlight the need for enhanced defense mechanisms, the exploration of novel attack strategies, the evaluation of attacks in different application domains, and the establishment of standardized benchmarks and evaluation criteria for physical adversarial attacks. Through this comprehensive survey, we aim to provide a valuable resource for researchers, practitioners, and policymakers to gain a holistic understanding of physical adversarial attacks in computer vision and facilitate the development of robust and secure DNN-based systems.

show abstract

Section: Imaging Device Manipulation-based Attackmentioning

confidence: 99%

Physical Adversarial Attacks for Camera-Based Smart Systems: Current Trends, Categorization, Applications, Research Challenges, and Future Outlook

Guesmi,

Hanif,

Ouni

et al. 2023

IEEE Access

View full text Add to dashboard Cite

show abstract

“…Digital cameras use rolling shutter technique to obtain high resolution images at higher rate and at cheaper price [3,29]. Rolling shutter technology is used in majority of consumer-grade cameras, such as the ones used in cellphones [21], AR glasses [34] as well as for machine vision [1,2].…”

Section: Human Visionmentioning

confidence: 99%

Invisible Perturbations: Physical Adversarial Examples Exploiting the Rolling Shutter Effect

Sayles¹,

Hooda²,

Gupta³

et al. 2020

Preprint

View full text Add to dashboard Cite

Physical adversarial examples for camera-based computer vision have so far been achieved through visible artifacts -a sticker on a Stop sign, colorful borders around eyeglasses or a 3D printed object with a colorful texture. An implicit assumption here is that the perturbations must be visible so that a camera can sense them. By contrast, we contribute a procedure to generate, for the first time, physical adversarial examples that are invisible to human eyes. Rather than modifying the victim object with visible artifacts, we modify light that illuminates the object. We demonstrate how an attacker can craft a modulated light signal that adversarially illuminates a scene and causes targeted misclassifications on a state-of-the-art ImageNet deep learning model. Concretely, we exploit the radiometric rolling shutter effect in commodity cameras to create precise striping patterns that appear on images. To human eyes, it appears like the object is illuminated, but the camera creates an image with stripes that will cause ML models to output the attackerdesired classification. We conduct a range of simulation and physical experiments with LEDs, demonstrating targeted attack rates up to 84%.

show abstract

“…The movement of the UAV during image acquisition causes a short delay between the top and bottom of the generated image (Gazioğlu et al, 2017). This causes a kind of image distortion known as the rolling/jelly artifact, which negatively affects the accuracy of the DEM produced by the structure from motion (SfM) approach (Bruno and Forlani, 2023;Incekara and Seker, 2021;Kim et al, 2020). On the other hand, depending on the stabilization of the UAV platform, its speed and the exposure time of the camera during image acquisition, motion blur may occur and SfM suffers from this distortion as well (Peng et al, 2022;Sieberth et al, 2015;Teague and Chahl, 2023).…”

Section: Introductionmentioning

confidence: 99%

Performance Analysis of a Drone Development Kit-derived Digital Elevation Model

Doğruluk,

Yalçın

2023

International Journal of Environment and Geoinformatics

View full text Add to dashboard Cite

Surface modeling constitutes is a crucial aspect in numerous engineering inquiries and earth observation endeavors. In contemporary times, the acquisition of geospatial data essential for the digital representation of local regions is increasingly facilitated through drone-based methodologies, supplanting conventional terrestrial data gathering techniques. The market presently hosts a plethora of cost-effective, "ready-to-fly" unmanned aerial vehicles (UAVs), offering users the capability to generate photogrammetric outputs, including high geometric precision Digital Elevation Models (DEMs). Moreover, modularly structured drone development kits, designed for multifarious applications, are readily accessible for purchase. These drone kits offer an economically advantageous platform that users can customize to suit their specific needs. Nevertheless, the geometric precision of DEMs created using these kits hinges upon the capabilities of the imaging and navigation systems, in addition to the stabilization of the platform during autonomous flight. In this study, using a drone development kit and a commercial drone, simultaneous image acquisition was performed for the same study area and two different DEMs were produced. The efficacy of the DEM generated using the drone development kit was assessed through a comparative analysis with the DEM obtained from a commercial drone. In addition, geometric accuracy assessment was conducted for both DEMs using ground control points. The findings reveal the usability of drone development kits in precision DEM production, as well as their limitations.

show abstract

Object Distance Estimation Using a Single Image Taken from a Moving Rolling Shutter Camera

Cited by 9 publications

References 30 publications

Physical Adversarial Attacks for Camera-Based Smart Systems: Current Trends, Categorization, Applications, Research Challenges, and Future Outlook

Physical Adversarial Attacks for Camera-Based Smart Systems: Current Trends, Categorization, Applications, Research Challenges, and Future Outlook

Invisible Perturbations: Physical Adversarial Examples Exploiting the Rolling Shutter Effect

Performance Analysis of a Drone Development Kit-derived Digital Elevation Model

Contact Info

Product

Resources

About