Recompression-based image forensics has been a largely investigated area in the forensic community over recent years. This mainly involves detecting the number of different compressions in an image by exploiting certain artifacts left behind by the compression stages. The artifacts left in images by Joint Photography Experts Group (JPEG) compression coding operations play an important role in passive forensic analysis of images, which help in assessing the authenticity of the image. In this regard, most of the solutions proposed to date are restricted to double compression detection in JPEG images. However, in practical cases, this restriction may not always work. This is because an image having undergone multiple stages of compression, including triple or higher degrees of compression, will be “missed” by such forgery detection algorithms. To address this issue, we propose a deep convolutional neural network (CNN) model that detects both double and triple compression in JPEG images. The model is trained with the compression features specific to single, double, and triple JPEG compressions. The trained model is capable of determining the authenticity of an image by distinguishing between tampered and untampered image regions. Further, the proposed model localizes the forged regions within an image, distinguishing between double and triple compressed regions. Our experimental results establish the efficiency of the proposed model.