AI image enhancer: Detecting and refining AI-generated images
Abstract
Recent advances in models like GANs or diffusion models have made it possible to generate photorealistic images, indistinguishable from real images. This has raised serious concerns about the truthfulness of media and about the ethics of producing or using these images. In our paper “AI Image Enhancer: Detecting and Refining AI-Generated Images” we address both the creation and detection of these images using a Deep Convolutional Generative Adversarial Network (DCGAN). The project has two main components. In the first stage of the project, the DCGANs will be used to create high quality synthetic images that look more realistic because they improve the texture, lighting, and overall appearance of the image. The second stage uses the discriminator unit of DCGAN to differentiate real images and the AI generated images from one another. This unit can capture subtle artifacts and inconsistencies. Our method makes sure that more realistic synthetic images are made by using DCGANs for both generation and detection. It also lets you find AI generated images.
Read the paper
Related studies
- Learning from First-Generation Qualitative Approaches in the IS Discipline: An Evolutionary View and Some Implications for Authors and Evaluators (PART 1/2)
- Commentary on the clinical and preclinical dosage limits of interstitially administered magnetic fluids for therapeutic hyperthermia based on current practice and efficacy models
- Prosthesis: New Technological Opportunities and Innovative Biomedical Devices
- The history and future of neural modeling for cochlear implants
- Ryanodine receptors: waking up from refractoriness
- Novel Transgenic Rabbit Model Sheds Light on the Puzzling Role of Matrix Metalloproteinase-12 in Atherosclerosis
Go deeper in Evidence Hub
Evidence Hub adds a study data sheet (design, sample size, evidence level, key numbers), an AI summary and a chat grounded in the paper's text.