The growing sophistication of deepfake technologies poses a serious threat to the authenticity of digital media and has far-reaching implications for security, privacy and misinformation. Existing deepfake datasets are often limited in scope, with most focusing on a single manipulation technique, and only a few addressing the specific domain of facial-reenactment. To address this gap, we present ReenactFaces, a specialized, open-access dataset designed to support the development and evaluation of deepfake detection systems targeting facial-reenactment methods. This dataset includes both real and manipulated videos, enabling researchers to train and test models specifically against reenactment-based deepfakes. ReenactFaces provides a valuable resource for improving the generalization of detection models to reenactment manipulations, filling a critical gap in the literature and complementing existing deepfake datasets. The dataset is available at: https://zenodo.org/records/14035828 .