Brand logos serve as essential visual identifiers and must adhere to standardized design guidelines to maintain consistency and recognizability. However, distortions such as occlusion, pixelation, discoloration, font alteration, and aspect ratio deformation can significantly compromise logo integrity. This study leverages deep learning models to detect logos, classify occlusions, restore damaged regions, and assess distortion levels. A synthetic dataset of 6,000 images was generated to simulate four distortion types: aspect ratio deformation, pixelation, font distortion, and discoloration. The proposed framework integrates four deep learning models: YOLOv8 for logo detection, ResNet-18 for occlusion classification, U-Net for segmentation, and a GAN for image restoration. Results show that YOLOv8 achieved an Intersection over Union (IoU) of 0.8567 and a mean Average Precision (mAP) of 0.9482, indicating high spatial accuracy in logo loc alization. ResNet-18 reached a 96.50% accuracy in occlusion classification. U-Net achieved an IoU of 0.8679 and a Dice coefficient of 0.9185, demonstrating effective mask generation. The GAN-based restoration module produced a PSNR of 33.36 dB and an MSE of 30.02 on synthetically occluded images. However, performance dropped significantly on real-world occlusions (PSNR: 9.79 dB, MSE: 6823.83), revealing the difficulty of generalization. Quantitative analysis of distortion severity across five samples revealed degradation of aspect ratio distortion (16.67–65.54%), pixelation (14.15–64.09%), font distortion (18.86–55.98%), and discoloration (2.62–63.98%). Overall, the framework presents a robust, multi-stage approach for automated logo integrity assessment, laying the groundwork for future research in real-world logo verification scenarios.