Metadata-Based Detection of Child Sexual Abuse Material
This work addresses the challenge of CSAM detection for law enforcement by enabling faster identification and blocking of distribution without requiring sensitive imagery, though it is incremental as it builds on existing metadata-based methods.
The paper tackles the problem of detecting Child Sexual Abuse Material (CSAM) by using file metadata instead of imagery to bypass ethical and legal constraints, achieving a 0.97 accuracy with a convolutional neural network model that is robust against adversarial evasion and generalizes well on open datasets.
Child Sexual Abuse Media (CSAM) is any visual record of a sexually-explicit activity involving minors. CSAM impacts victims differently from the actual abuse because the distribution never ends, and images are permanent. Machine learning-based solutions can help law enforcement quickly identify CSAM and block digital distribution. However, collecting CSAM imagery to train machine learning models has many ethical and legal constraints, creating a barrier to research development. With such restrictions in place, the development of CSAM machine learning detection systems based on file metadata uncovers several opportunities. Metadata is not a record of a crime, and it does not have legal restrictions. Therefore, investing in detection systems based on metadata can increase the rate of discovery of CSAM and help thousands of victims. We propose a framework for training and evaluating deployment-ready machine learning models for CSAM identification. Our framework provides guidelines to evaluate CSAM detection models against intelligent adversaries and models' performance with open data. We apply the proposed framework to the problem of CSAM detection based on file paths. In our experiments, the best-performing model is based on convolutional neural networks and achieves an accuracy of 0.97. Our evaluation shows that the CNN model is robust against offenders actively trying to evade detection by evaluating the model against adversarially modified data. Experiments with open datasets confirm that the model generalizes well and is deployment-ready.