论文标题
在数字法医调查中应用人工智能进行年龄估计
Applying Artificial Intelligence for Age Estimation in Digital Forensic Investigations
论文作者
论文摘要
儿童性虐待和剥削（CSAE）受害者的确切年龄估计是最重要的数字法医挑战之一。调查人员通常需要通过查看图像并解释性发展阶段和其他人类特征来确定受害者的年龄。巨大的法医积压，认知偏见以及这项工作可能带来的巨大心理压力，通常会受到负面影响。本文评估了现有的面部图像数据集，并提出了一个针对类似数字法医研究贡献需求的新数据集。这个由0至20岁个体的小型数据集包含245张图像，并与FG-NET数据集中的82张唯一图像合并，从而达到了总共327张具有高图像多样性和低年龄范围密度的图像。对IMDB Wiki数据集预先训练的深度期望（DEX）算法进行了测试。 16至20岁的年轻青少年/成年人的年轻青少年的总体结果非常令人鼓舞 - 达到低至1.79的MAE，但也表明，0至10岁儿童的准确性需要进一步的工作。为了确定原型的功效，已经考虑了四个数字法医专家的宝贵输入，包括两名法医研究者，以提高年龄估计结果。需要进一步的研究来扩展有关图像密度和性别多样性等因素的平等分布的数据集。
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.
