With the proliferation of data, particularly on social networks, the accuracy of information becomes uncertain. In this context, a major challenge lies in detecting image manipulations, where alterations are made to deceive observers. Aligning with the anomaly detection issue, recent methods approach the detection of image transformations as a community detection problem within graphs associated with the images. In this study, we propose using a community clustering method based on non-negative symmetric matrix factorization. By examining several experiments detecting alterations in manipulated images, we assess the method's robustness and discuss potential enhancements.