Social media (SM) has emerged as a critical tool in disaster response, offering real-time visual insights through image sharing. This visual information aids responders in assessing the severity of situation and formulating effective strategies. However, the prevalence of forged images on SM poses a significant challenge, potentially misleading responders and hindering the humanitarian efforts. Therefore, it’s crucial to verify the credibility of information sourced from SM images before incorporating it into any crucial decision-making process. However, detecting forged disaster images uploaded to SM platforms presents additional challenges. These images undergo various post-processing operations including compression, which introduces additional noise and degrades image quality, thereby complicates forgery detection. This study is the first to focus on SM disaster image Forgery detection. A novel dataset named Forge Disaster is presented, comprising both authentic and forged SM images with copy-move and splicing forgeries. The primary objective of this dataset is to serve as a benchmark for evaluating novel techniques and methodologies in the domain. Additionally, this paper presents a unified approach for robust detection of both copy-move and spliced disaster images on SM. Leveraging image enhancement filters, local binary pattern (LBP) combined with discrete fourier transform (DFT), and support vector machine (SVM), the proposed approach achieved an impressive detection accuracy of 91%, outperforming existing forgery detection methods. These contributions address the growing concern of misinformation through forged images on SM platforms during disaster situations, enhancing the reliability of disaster-related information for effective response.