A Unified Network for Detecting Out-Of-Context Information Using Generative Synthetic Data.

In our modern world, the manipulation of digital content, especially the usage of out-of-context images known as Cheapfakes, has become a significant challenge for the endorsement of integrity and trustworthiness for information on the Internet. This highlights an urgent need for effective detection of the misuse of images and their accompanied captions. Motivated by this issue, our research presents an innovative approach to the solution of this problem. Participating in the ACM ICMR 2024 Grand Challenge on Detecting Cheapfakes, we leverage a unified end-to-end network, integrated with generative synthetic data for training. After complete evaluation, our proposed network demonstrated a remarkable accuracy of 95.60% on the public test dataset for Task 1, as well as efficiency in Task 2. This paper highlights the notable potential of employing an end-to-end network for Cheapfakes detection, which composes a significant contribution to the advancement of multimedia content integrity. Our source code is publicly available at https://github.com/thanhson28/cheapfakes_detection_icmr2024.git