|
The proliferation of artificial intelligence (AI) has revolutionised various fields, including image processing and manipulation. While AI-driven image manipulation techniques such as DALL-E1 and Stable Diffusion2 offer unprecedented capabilities for creativity and visual enhancement, they also pose significant challenges in terms of authenticity, integrity, and misinformation. Advancements in AI Image modification have blurred the line between reality and fiction, raising concerns about the potential for misinformation, propaganda, and manipulation in various domains, including journalism, advertising, and social media. These techniques enable the creation of visually convincing yet falsified images which pose challenges for current state-of-the-art techniques such as early/late fusion3 which often struggle to discern subtle alterations made by AI algorithms and as such report poor detection results, necessitating the development of advanced detection methods capable of discerning AI manipulations.
This paper presents a dataset of images containing AI generated modifications and a method for the detection of AI manipulations in images. The dataset consists of over 14,000 AI manipulated images and their ground truth masks which indicate where on the images the manipulations take place. The modified images were created using a state-of-the-art text-guided generation method4 that can generate modifications within an image from a text prompt. This dataset facilitates the development of a new deep-learning based image manipulation detection model which can reliably determine the existence of edited content in an image and localise the area that has been modified.
|
