Generating adversarial images for image cropping models (IMAGE)
Caption
Many commercial image cropping models utilize saliency maps (also known as gaze estimation) to identify the most critical areas within an image. In this study, researchers developed innovative techniques to introduce imperceptible noisy perturbations into images, thus influencing the output of cropping models. This approach aims to prevent essential parts of images, such as copyright information or watermarks, from being inadvertently cropped, thus promoting fairness in AI models.
Credit
Masatomo Yoshida from Doshisha University
Usage Restrictions
Credit must be given to the creator
License
Original content