This paper analyzes the common issue of exam cheating in offline modes in classrooms and explores how deep learning approaches can offer viable solutions. Even though exam integrity is essential to fair evaluation and merit-based practices, conventional monitoring techniques often fail to spot complex forms of cheating, especially in the context of the COVID19 pandemic that increased opportunities for academic dishonesty as a result of the transition to remote learning. Deep learning techniques are able to quickly and accurately analyze large amounts of data by utilizing artificial intelligence and machine learning. This makes it easier to create effective monitoring systems that can identify different kinds of dishonest activity. The aim of this study is to show how deep learning techniques may effectively minimize cheating and maintain the integrity of offline exams.