## 2 Related Work
### Robustness of DL Models on Image Perturbation  
Robustness analysis of DL models on noisy images from different communication systems for CV applications are also considered in this study. In this section, we will discuss the related work based on robustness of DL models on images with different types of noise perturbation. Despite the success of Deep Neural Network (DNN) with complex and high-dimensional image data, they are still not robust against image perturbations. Authors in [33] were the first to discover the vulnerability of DNNs to certain input perturbations, which result in significant discontinuities in their outputs. These perturbations were named _adversarial examples_ and were found to cause a wide range of deep learning models to misclassify, regardless of the different model architecture or training data. The authors also examined several interesting properties of neural networks, including their capacity for learning intricate functions, the existence of adversarial examples that can confuse the network, and their ability to generalize and extrapolate. Authors in [34] carry out further studies on adversarial models and present defense systems, such as adversarial training, input transformations, and robust optimization to minimize the effect of adversarial examples. Among various image perturbation, visible noise on digital images is a type of perturbation resulting from perceptible alterations made to the image pixels, and it can have an impact on DL-based image classification tasks. The introduction of visible noise in digital images during the processes of its acquisition, encoding, transmission, and processing is an inherent consequence of the utilization of electronic components, particularly sensors and actuators [35; 36]. A comprehensive overview of digital image noise models can be found in [36], which highlights various types of noise that can impact digital images, such as additive Gaussian noise, quantization noise, color quantization with dither, salt-pepper noise, Rayleigh noise, gamma noise, uniform noise (white noise), and Poisson noise. The paper also discusses methods for modeling and analyzing these noises using probability density functions and statistical models, and common techniques for removing them, such as spatial filtering, frequency filtering, and wavelet denoising. Deep learning has made remarkable advancements in various domains, particularly in image classification. However, the accuracy of these models degrades when images are subjected to such distortions and noise [37]. Authors in [37] evaluated various factors that contribute to image quality, including resolution, noise, and blur, and evaluated their individual impacts on DNNs. The outcomes indicated that noise and blur have a stronger impact on DNNs than other factors. The authors also presented strategies, such as pre-processing and data augmentation, for improving DNN performance on low-quality images. Authors in [38] evaluated the robustness of Convolutional Neural Networks (CNN) to various types of image degradations, such as Gaussian noise, salt and pepper noise, Joint Photographic Experts Group (JPEG) compression, and motion-blur. The results showed that CNNs are generally more robust to image degradation compared to traditional methods, but they still have limitations. To address these limitations, the authors proposed a CNN architecture called a capsule network, which was shown to be more robust to degradation. Authors in [39] investigate the effect of color information on the robustness of CNNs for image classification tasks. They have compared the performance of CNNs trained on color images and grayscale images when subjected to degradation, such as Gaussian noise and JPEG compression. The results show that CNNs trained on color images are more robust to degradation compared to those trained on grayscale images, due to the additional cues provided by the color information. In CV tasks, CNNs have shown outstanding performance when applied to identically and independently distributed data.