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With the massive improvement in deep learning, application areas are also expanding significantly particularly for multimedia information forensic, antiforensic, and counter anti-forensic in the field of forensic analysis. Generative adversarial network (GAN) is one of the most popular deep learning models which is widely being used for anti-forensic JPEG compressed images to generate ground truth like images for making fool the JPEG compression detector. In this paper, we analyze the generator and discriminator in the GAN model to generate more realistic images and detect between generated and original images. We investigate the proposed method using two different GAN models in which the generators and discriminators are trained separately in each model. Then we use the generated images produced by the generator in one model to detect whether it is generated or original images with discriminator in another model and vice-versa. The reconstructed images produced by both generators are more realistic in visual perception and have the better quality that can deceive the JPEG compression detector. The discriminators are capable of differentiating between the generated and real images only if the generated images are reconstructed by their own generators. If we try to classify the generated images obtained by the generator in one model using discriminator in another model, the discrimination results reduce at an alarming rate.
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