Metadata-Based Detection of Child Sexual Abuse Material

• URL: http://arxiv.org/abs/2010.02387v2
• Date: Wed, 27 Oct 2021 20:20:24 GMT
• Title: Metadata-Based Detection of Child Sexual Abuse Material
• Authors: Mayana Pereira, Rahul Dodhia, Hyrum Anderson and Richard Brown
• Abstract summary: Child Sexual Abuse Media (CSAM) is any visual record of a sexually-explicit activity involving minors. We propose a framework for training and evaluating deployment-ready machine learning models for CSAM identification.
• Score: 1.1470070927586016
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• Unlearn and Burn: Adversarial Machine Unlearning Requests Destroy Model Accuracy [65.80757820884476]
  We expose a critical yet underexplored vulnerability in the deployment of unlearning systems. We present a threat model where an attacker can degrade model accuracy by submitting adversarial unlearning requests for data not present in the training set. We evaluate various verification mechanisms to detect the legitimacy of unlearning requests and reveal the challenges in verification.
  arXiv  Detail & Related papers  (2024-10-12T16:47:04Z)
• MisGUIDE : Defense Against Data-Free Deep Learning Model Extraction [0.8437187555622164]
  "MisGUIDE" is a two-step defense framework for Deep Learning models that disrupts the adversarial sample generation process. The aim of the proposed defense method is to reduce the accuracy of the cloned model while maintaining accuracy on authentic queries.
  arXiv  Detail & Related papers  (2024-03-27T13:59:21Z)
• Small Effect Sizes in Malware Detection? Make Harder Train/Test Splits! [51.668411293817464]
  Industry practitioners care about small improvements in malware detection accuracy because their models are deployed to hundreds of millions of machines. Academic research is often restrained to public datasets on the order of ten thousand samples. We devise an approach to generate a benchmark of difficulty from a pool of available samples.
  arXiv  Detail & Related papers  (2023-12-25T21:25:55Z)
• From Zero to Hero: Detecting Leaked Data through Synthetic Data Injection and Model Querying [10.919336198760808]
  We introduce a novel methodology to detect leaked data that are used to train classification models. textscLDSS involves injecting a small volume of synthetic data--characterized by local shifts in class distribution--into the owner's dataset. This enables the effective identification of models trained on leaked data through model querying alone.
  arXiv  Detail & Related papers  (2023-10-06T10:36:28Z)
• Can Membership Inferencing be Refuted? [31.31060116447964]
  We study the reliability of membership inference attacks in practice. We show that a model owner can plausibly refute the result of a membership inference test on a data point $x$ by constructing a proof of repudiation. Our results call for a re-evaluation of the implications of membership inference attacks in practice.
  arXiv  Detail & Related papers  (2023-03-07T04:36:35Z)
• Are You Stealing My Model? Sample Correlation for Fingerprinting Deep Neural Networks [86.55317144826179]
  Previous methods always leverage the transferable adversarial examples as the model fingerprint. We propose a novel yet simple model stealing detection method based on SAmple Correlation (SAC) SAC successfully defends against various model stealing attacks, even including adversarial training or transfer learning.
  arXiv  Detail & Related papers  (2022-10-21T02:07:50Z)
• Quo Vadis: Hybrid Machine Learning Meta-Model based on Contextual and Behavioral Malware Representations [5.439020425819001]
  We propose a hybrid machine learning architecture that simultaneously employs multiple deep learning models. We report an improved detection rate, above the capabilities of the current state-of-the-art model.
  arXiv  Detail & Related papers  (2022-08-20T05:30:16Z)
• RelaxLoss: Defending Membership Inference Attacks without Losing Utility [68.48117818874155]
  We propose a novel training framework based on a relaxed loss with a more achievable learning target. RelaxLoss is applicable to any classification model with added benefits of easy implementation and negligible overhead. Our approach consistently outperforms state-of-the-art defense mechanisms in terms of resilience against MIAs.
  arXiv  Detail & Related papers  (2022-07-12T19:34:47Z)
• CARLA-GeAR: a Dataset Generator for a Systematic Evaluation of Adversarial Robustness of Vision Models [61.68061613161187]
  This paper presents CARLA-GeAR, a tool for the automatic generation of synthetic datasets for evaluating the robustness of neural models against physical adversarial patches. The tool is built on the CARLA simulator, using its Python API, and allows the generation of datasets for several vision tasks in the context of autonomous driving. The paper presents an experimental study to evaluate the performance of some defense methods against such attacks, showing how the datasets generated with CARLA-GeAR might be used in future work as a benchmark for adversarial defense in the real world.
  arXiv  Detail & Related papers  (2022-06-09T09:17:38Z)
• Defending against Model Stealing via Verifying Embedded External Features [90.29429679125508]
  adversaries can steal' deployed models even when they have no training samples and can not get access to the model parameters or structures. We explore the defense from another angle by verifying whether a suspicious model contains the knowledge of defender-specified emphexternal features. Our method is effective in detecting different types of model stealing simultaneously, even if the stolen model is obtained via a multi-stage stealing process.
  arXiv  Detail & Related papers  (2021-12-07T03:51:54Z)
• Trade-offs between membership privacy & adversarially robust learning [13.37805637358556]
  We identify settings where standard models will overfit to a larger extent in comparison to robust models. The degree of overfitting naturally depends on the amount of data available for training.
  arXiv  Detail & Related papers  (2020-06-08T14:20:12Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.