The precise age estimation of child sexual abuse and exploitation (CSAE) victims is one of the most significant digital forensic challenges. Investigators often need to determine the age of victims by looking at images and interpreting the sexual development stages and other human characteristics. The main priority - safeguarding children -- is often negatively impacted by a huge forensic backlog, cognitive bias and the immense psychological stress that this work can entail. This paper evaluates existing facial image datasets and proposes a new dataset tailored to the needs of similar digital forensic research contributions. This small, diverse dataset of 0 to 20-year-old individuals contains 245 images and is merged with 82 unique images from the FG-NET dataset, thus achieving a total of 327 images with high image diversity and low age range density. The new dataset is tested on the Deep EXpectation (DEX) algorithm pre-trained on the IMDB-WIKI dataset. The overall results for young adolescents aged 10 to 15 and older adolescents/adults aged 16 to 20 are very encouraging -- achieving MAEs as low as 1.79, but also suggest that the accuracy for children aged 0 to 10 needs further work. In order to determine the efficacy of the prototype, valuable input of four digital forensic experts, including two forensic investigators, has been taken into account to improve age estimation results. Further research is required to extend datasets both concerning image density and the equal distribution of factors such as gender and racial diversity.
In many applications of forensic image analysis, state-of-the-art results are nowadays achieved with machine learning methods. However, concerns about their reliability and opaqueness raise the question whether such methods can be used in criminal investigations. So far, this question of legal compliance has hardly been discussed, also because legal regulations for machine learning methods were not defined explicitly. To this end, the European Commission recently proposed the artificial intelligence (AI) act, a regulatory framework for the trustworthy use of AI. Under the draft AI act, high-ri…
University of Brasilia（巴西利亚大学）；Federal University of Maranhao（马拉尼昂联邦大学）；KU Leuven（鲁汶大学）；National Institute of Criminalistics（国家刑事科学研究所）；Brazilian Federal Police（巴西联邦警察）
Facial analysis permits many investigations some of the most important of which are craniofacial identification, facial recognition, and age and sex estimation. In forensics, photo-anthropometry describes the study of facial growth and allows the identification of patterns in facial skull development by using a group of cephalometric landmarks to estimate anthropological information. In several areas, automation of manual procedures has achieved advantages over and similar measurement confidence as a forensic expert. This manuscript presents an approach using photo-anthropometric indexes, gene…
FACT: A Forensic Agent with Compiled Tool-Use Trajectories for AI-Generated Image Detection
Lingnan University（岭南大学）；Harbin Institute of Technology, Shenzhen（哈尔滨工业大学（深圳））；The University of Hong Kong（香港大学）；The Chinese University of Hong Kong（香港中文大学）；Shenzhen University（深圳大学）
Agentic Tool-Augmented Reasoning for Explainable Image Forgery Detection
Nanyang Technological University（南洋理工大学）；Ant Digital Technologies, Ant Group（蚂蚁数字科技，蚂蚁集团）；Singapore University of Technology and Design（新加坡科技设计大学）；Singapore Management University（新加坡管理大学）
A practical artificial intelligence framework for legal age estimation using clavicle computed tomography scans
Panacea Cooperative Research S.Coop.；Department of Computer Science and Artificial Intelligence, University of Granada（计算机科学与人工智能系，格拉纳达大学）；Andalusian Research Institute in Data Science and Computational Intelligence (DaSCI Institute)（数据科学与计算智能安达卢西亚研究所（DaSCI研究所））；Department of Organisms and Systems Biology, Faculty of Biology, University of Oviedo（生物系，奥维耶多大学 organisms and Systems Biology 系）；Faculty of Computer Science, CITIC, University of A Coruña（计算机科学系，阿拉斯加州立大学）
Tell-Tale Watermarks for Explanatory Reasoning in Synthetic Media Forensics
Information and Society Research Division, National Institute of Informatics（信息与社会研究部，国家信息研究所）；Graduate School of Information Science and Technology, University of Tokyo（东京大学信息科学与技术研究生院）
FakeScope: Large Multimodal Expert Model for Transparent AI-Generated Image Forensics
College of Computing, City University of Hong Kong（城市大学 computing 学院）；School of Data Science and Artificial Intelligence, Chang’an University（长安大学数据科学与人工智能学院）；School of Computer Science and Engineering, Ministry of Education Key Laboratory of Information Technology, Guangdong Province Key Laboratory of Information Security Technology, Sun Yat-Sen University（中山大学计算机科学与工程学院）；College of Computing and Data Science, Nanyang Technological University（南洋理工大学 computing 与数据科学学院）；Artificial Intelligence Lab. (Recod.ai) at the University of Campinas（坎皮纳斯大学人工智能实验室）
Bernoulli Institute for Mathematics, Computer Science and Artificial Intelligence, University of Groningen（格罗宁根大学伯努利数学、计算机科学与人工智能研究所）；Universidad de León（莱昂大学）
One of the challenging problems in digital image forensics is the capability to identify images that are captured by the same camera device. This knowledge can help forensic experts in gathering intelligence about suspects by analyzing digital images. In this paper, we propose a two-part network to quantify the likelihood that a given pair of images have the same source camera, and we evaluated it on the benchmark Dresden data set containing 1851 images from 31 different cameras. To the best of our knowledge, we are the first ones addressing the challenge of device-based image matching. Though…
