Detecting Out-of-Context Media with LLaMa-Adapter V2 and RoBERTa: An Effective Method for Cheapfakes Detection
Hoa-Vien Vo-Hoang, Long-Khanh Pham, Minh-Son Dao · 2024
Cheapfakes is a new term for fake media that is made without AI, but with simple tools or captions that deceive or mislead. Cheapfakes include photos, videos, audio recordings, or any media that has been changed to distort its original meaning or context. The "ACM ICMR 2024 Grand Challenge on Detecting Cheapfakes" focuses on the challenge of finding out-of-context (OOC) media, which can assist fact-checkers in their work. By finding OOC media, we can narrow down the search space and raise the chances of discovering cheapfakes. To cope with this challenge, in this research paper, our team propose a novel approach that combines Context Generation and Text Classification as the key components in detecting cheapfakes. Our method was evaluated using 80% of the public test set from the COSMOS dataset, which includes two context labels: out-of-context and not-out-of-context. The results demonstrate an accuracy of 85.9%, validating the effectiveness and reliability of our method in identifying cheapfakes.