法医学研究ジャーナル

Objective: This study attempts to find craniometric landmark measurements that can be standardized and used to identify the ethnicity/race ‘Hispanic’ when unknown crania are found by the police.

Methods: Craniometric measurements (n=31) were collected from a small sample (n=13) of documented Hispanic crania curated at the Maxwell Museum of Anthropology, Albuquerque, New Mexico. These craniometric measurements, despite documented population affiliation, were analyzed by the FORDISC 3.1 computer program to verify ancestry. The 31 craniometric measurements of the Maxwell Museum sample were converted to means, and then analyzed in FORDISC 3.1 Forensic Data Base (FDB). Subsequently, the craniometric means for the Maxwell Museum Hispanics were compared to the craniometric means calculated by FORDISC 3.1 FDB for Hispanic males (n=148) and females (n=28) and Guatemalan males (n=66) in order to find craniometric measurement landmarks that could be important in identification of ‘Hispanic.’

Results: On the first run, FORDISC 3.1 classified Maxwell Museum Hispanics into the American Indian Male (AM) reference group with a posterior probability of 0.284. Regardless, graph of the results depicted in 3D canonical space showed the Maxwell Museum Hispanic sample was closest to the Hispanic male reference group centroid. Furthermore, seven craniometric measurement means computed by SPSS statistical software were nearly identical to each other and could be key in identifying Hispanic crania. These measurements were ZYB; BNL; WFB; NLB; EKB; OCC; and FOB.

Conclusion: Forced migrations spurred by totalitarian regimes in the country of origin or drought and starvation has resulted in migrant fatalities, whether at the US–Mexico border crossing or at sea between Cuba or Haiti and Miami. This research will add a new perspective in using craniometrics to study admixture in general and Hispanic identification in particular, and simultaneously help law enforcement reduce the number of open cases that deal with questionable ancestries.

The proliferation of internet usage has led to an increase in deepfake attacks, posing significant threats to privacy and data security. Existing detection systems are continually challenged by increasingly sophisticated deepfake techniques. In this paper, we propose a novel method for detecting deepfake anomalies by focusing on the lip region of human faces in videos. This area is often subtle and difficult for humans to scrutinize. Our approach integrates the Minimum Covariance Determinant (MCD) Estimator with the SHA-256 hashing algorithm and RAID technology to identify even the slightest deepfake activities. By employing the Lip Shaping Technique, we evaluate the effectiveness of our method. Experimental results demonstrate the proposed method’s promising performance and its significant impact on frame processing speed due to the incorporation of optimized storage techniques.