Image Safety Classifier

Image safety classifiers aim to automatically identify and flag inappropriate content in images, such as violence, hate speech, or sexually explicit material. Current research focuses on improving the accuracy and robustness of these classifiers, particularly when dealing with AI-generated images which exhibit different characteristics than real-world images. This involves developing new benchmark datasets and exploring model architectures that combine visual and language understanding, often incorporating techniques like counterfactual explanations to justify the classification decisions and minimize unnecessary obfuscation. The development of effective image safety classifiers is crucial for mitigating the spread of harmful content online and fostering safer digital environments.