The text on the ResearchGate website reads:"Verifying You Are Human: A Review of the State-of-the-Art in Image-Based Human VerificationHuman verification is an essential component of many applications, including online identity verification, access control, and fraud detection. In recent years, there has been a growing interest in image-based human verification methods due to their potential for high accuracy and robustness against spoofing attacks. However, there are still several challenges that need to be addressed, such as the difficulty in distinguishing between real and fake images, and the lack of standardization in evaluation protocols.In this review, we provide a comprehensive overview of the state-of-the-art techniques for image-based human verification, including deep learning methods, template matching, and graph-based approaches. We also discuss the limitations and challenges of each method and identify future research directions. Our goal is to provide a useful resource for researchers and practitioners working in this area, and to help advance the field towards more accurate and reliable human verification methods."In summary, the article reviews the state-of-the-art techniques for image-based human verification, including deep learning methods, template matching, and graph-based approaches. It discusses the limitations and challenges of each method and identifies future research directions to advance the field towards more accurate and reliable human verification methods.