The recent advancements in the research area demonstrate a significant shift towards leveraging multi-modal approaches to enhance the robustness and performance of various machine learning models. A common theme across several papers is the integration of textual and visual data to address challenges in tasks such as image classification, super-resolution, and adversarial defense. These multi-modal methods aim to improve semantic alignment and coherence, leading to more accurate and reliable outcomes. Notably, the use of large language models in conjunction with visual data has shown promise in detecting and mitigating adversarial attacks, as well as in enhancing the interpretability of model predictions. Additionally, there is a growing focus on the resilience of digital forensic artifacts and the explainability of tampered text detection, highlighting the importance of considering tamper resistance and providing clear explanations for model decisions. Overall, the field is progressing towards more sophisticated, multi-modal, and interpretable solutions that advance the state-of-the-art in various domains.