Environment perception has emerged as a crucial application, driving significant efforts in both the industry and research community. The focus on developing powerful deep learning (DL)-based solutions has been particularly evident in applications such as autonomous robots and intelligent transportation systems. Designing reliable recognition systems is among the primary challenges in achieving robust environment perception. In this context, automotive cameras play a pivotal role, along with associated perception models such as object detectors and image classifiers, forming the foundation for vision-based perception modules. These modules are instrumental in gathering essential information about the environment, aiding autonomous vehicles (AVs) in making critical decisions for safe driving.

The pursuit of dependable recognition systems represents a major hurdle in establishing high-performance environment perception. The accuracy and reliability of such systems are critical for the safe operation of AVs and ensuring their successful integration into real-world scenarios. However, it has been demonstrated that these recognition systems are susceptible to adversarial attacks, which can undermine their integrity and pose potential risks to AVs and their passengers. In light of these challenges, addressing the vulnerabilities of DL-based environment perception systems to adversarial attacks becomes paramount. Research and development efforts must focus on building robust defense mechanisms to fortify these systems against potential threats, enabling the safe and trustworthy deployment of AVs and intelligent transportation systems in the future.

Digital Adversarial Attacks

In scenarios involving digital attacks, the attacker has the flexibility to manipulate the input image of a victim deep neural network (DNN) at the pixel level. These attacks assume that the attacker has access to the DNN’s input system, such as a camera or other means of providing input data. The concept of adversarial examples, where a small, imperceptible noise is injected to shift the model’s prediction towards the wrong class, was first introduced by Szegedy et al. [12]^[11]. Over time, various algorithms for creating adversarial examples have been developed, leading to the advancement of digital attacks. Some notable digital attacks include Carlini and Wagner’s attack (CW) ^[5], Fast Gradient Sign Method (FGSM) ^[3], Basic Iterative Method (BIM) [13]^[12], local search attacks [14]^[13], and HopSkipJump attack (HSJ) [15]^[14], among others.

Physical Adversarial Attacks

A physical attack involves adding perturbations in the physical space to deceive the target model. The process of crafting a physical perturbation typically involves two main steps. First, the adversary generates an adversarial perturbation in the digital space. Then, the goal is to reproduce this perturbation in the physical space, where it can be perceived by sensors such as cameras and radars, effectively fooling the target model. Existing methods for adding adversarial perturbations in different locations can be categorized into four main groups: Attack by Directly Modifying the Targeted Object: In this approach, the attacker directly modifies the targeted object to introduce the adversarial perturbation. For example, adversarial clothing has been proposed, where clothing patterns are designed to confuse object detectors [16,17,18]^[15][16][17]. Hu et al. [16]^[15] leverage pretrained GAN models to generate realistic/naturalistic images that can be printed on t-shirts and are capable of hiding the person wearing them. Guesmi et al. [17]^[16] proposed replacing the GAN with a semantic constraint based on adding a similarity term to the loss function and, in doing so, directly manipulating the pixels of the image. This results in a higher flexibility to incorporate multiple transformations. Attack by Modifying the Background: Adversarial patches represent a specific category of adversarial perturbations designed to manipulate localized regions within an image, aiming to mislead classification models. These attacks leverage the model’s sensitivity to local alterations, intending to introduce subtle changes that have a substantial impact on the model’s predictions. By exploiting the model’s reliance on specific image features or patterns, adversaries can create patches that trick the model into misclassifying the image or perceiving it differently from its actual content. An example of a practical attack for real-world scenarios is AdvPatch ^[10]. This attack creates universal patches that can be applied anywhere. Additionally, the attack incorporates Expectation over Transformation (EOT) [19]^[18] to enhance the robustness of the adversarial patch. The AdvCam technique ^[8] presents an innovative approach to image perturbation, operating within the style space. This method combines principles from neural style transfer and adversarial attacks to craft adversarial perturbations that seamlessly blend into an image’s visual style. For instance, AdvCam can introduce perturbations such as rust-like spots on a stop sign, making them appear natural and inconspicuous within their surroundings. Modifying the Camera: This method involves modifying the camera itself to introduce the adversarial perturbation. One approach is to leverage the Rolling Shutter Effect, where the timing of capturing different parts of the image is manipulated to create perturbations [20,21]^[19][20]. Modifying the Medium Between the Camera and the Object: This category includes attacks that modify the medium between the camera and the object. For instance, light-based attacks use external light sources to create perturbations that are captured by the camera and mislead the target model [6,22]^[6][21]. The Object Physical Adversarial Device (OPAD) [22]^[21] employs structured lighting methods to alter the appearance of a targeted object. This attack system is composed of a cost-effective projector, a camera, and a computer, enabling the manipulation of real-world objects in a single shot. Zhong et al. ^[6] harness the natural phenomenon of shadows to create adversarial examples. This method is designed to be practical in both digital and physical contexts. Unlike traditional gradient-based optimization algorithms, it employs optimization strategies grounded in particle swarm optimization (PSO) [23]^[22]. The researchers conducted extensive assessments in both simulated and real-world scenarios, revealing the potential threat posed by shadows as a viable avenue for attacks. However, it is important to note that these techniques may experience reduced effectiveness under varying lighting conditions. The Adversarial Color Film (AdvCF) method, introduced by Zhang et al. ^[7], utilizes a color film positioned between the camera lens and the subject of interest to enable effective physical adversarial attacks. By adjusting the physical characteristics of the color film without altering the appearance of the target object, AdvCF aims to create adversarial perturbations that maintain their effectiveness in various lighting conditions, including both daytime and nighttime settings. FakeWeather ^[9] attack aims to emulate the effects of various weather conditions, such as rain, snow, and hail, on camera lenses. This attack seeks to deceive computer vision systems, particularly those used in autonomous vehicles and other image-based applications, by adding perturbations to the captured images that mimic the distortions caused by adverse weather. In the FakeWeather attack, the adversary designs specific masks or patterns that simulate the visual artifacts produced by different weather conditions. These masks are then applied to the camera’s images, introducing distortions that can mislead image recognition models. The goal is to make the images appear as if they were captured in inclement weather, potentially causing the models to make incorrect predictions or classifications. One limitation of the FakeWeather attack is that the generated noise or perturbations may have unrealistic and pixelated patterns, which could potentially be detected by more robust image recognition systems. Additionally, the attack’s effectiveness may be limited to specific scenarios and image sizes, as it was initially tested on small images of 32 × 32 pixels from the CIFAR-10 dataset. Additionally, researchers have explored techniques to create adversarial perturbations with natural styles to ensure stealthiness and legitimacy to human observers. Such approaches aim to make the perturbations appear as natural phenomena in the scene.