Chromatic aberration is a common optical distortion in digital imaging that manifests as color fringing along edges in photographs, impacting image quality and visual perception. Traditional methods for detecting chromatic aberration rely on handcrafted features and image processing techniques, which often struggle with complex scenes or varied lighting conditions. In this paper, we propose a novel approach using fully convolutional networks (FCNs) to automatically detect chromatic aberration in digital images. The deep learning model is trained, allowing it to learn and adapt to the complex patterns associated with chromatic aberration. We evaluated the performance of the proposed method on a representative dataset of images with chromatic aberration and found that the FCN-based achieves similar identification accuracy as state-of-the-art methods, but is much faster.