Architecture of Afterlife: Future Cemetery in Metropolis

Do you believe soul never dies? Do you believe in afterlife? Think but do not answer. How do you remember a loved one who passed away? How would you like to remember a loved one who has passed away? Think but do not answer. Life and Death presents an eternal topic for human beings to study. Cemeteries, human being’s last stop and final dwellings, is the primary type of funerary architecture. Contemporary Cemetery is facing a severe challenge, namely the shortage of burial space, especially in Metropolis such as London, Tokyo, Hong Kong, and etc. This severe dearth of permanent burial grounds, coupled with the high cost of rental ash holding agencies has struck a near fatal blow to the contemporary cemetery development. In my opinion, the options for future cemeteries are either change, shrink or disappear. This doctor dissertation will mainly discuss the major problems of the conventional and contemporary cemetery and use the latest relevance case studies to explore and propose design guidelines for future cemetery design. The design research chapter will show an example of how to apply these guidelines into the architecture project in order to make the future cemetery carry on the culture and spirit aspect along with innovative technologies

Robustness and Generalizability of Deepfake Detection: A Study with Diffusion Models

The rise of deepfake images, especially of well-known personalities, poses a serious threat to the dissemination of authentic information. To tackle this, we present a thorough investigation into how deepfakes are produced and how they can be identified. The cornerstone of our research is a rich collection of artificial celebrity faces, titled DeepFakeFace (DFF). We crafted the DFF dataset using advanced diffusion models and have shared it with the community through online platforms. This data serves as a robust foundation to train and test algorithms designed to spot deepfakes. We carried out a thorough review of the DFF dataset and suggest two evaluation methods to gauge the strength and adaptability of deepfake recognition tools. The first method tests whether an algorithm trained on one type of fake images can recognize those produced by other methods. The second evaluates the algorithm's performance with imperfect images, like those that are blurry, of low quality, or compressed. Given varied results across deepfake methods and image changes, our findings stress the need for better deepfake detectors. Our DFF dataset and tests aim to boost the development of more effective tools against deepfakes.Comment: 8 pages, 5 figure