Abstract

Over the last decade, advancements in deep learning and computer vision have led to a tremendous growth in performance at the tasks of automated human age estimation and nudity detection. Modern machine learning models can predict whether or not an image contains nudity or the presence of a minor with startling accuracy. When used in conjunction, these technological advancements can be used to identify new instances of child pornography without ever coming into contact with the illicit material during model training. In this thesis, a label distribution learning framework for modeling human apparent age is proposed. Instead of directly modeling a person's biological age, we use a probability distribution over a sample of humans guessing how old that person looks like as the ground truth. This allows us to better capture the subjective nature of a person's age and advance state of the art performance at the task of apparent age estimation. Next, we introduce a framework to automatically identify Sexually Exploitative Imagery of Children (SEIC) in both images and video. It is a synthesis of our original age estimation models and Yahoo!'s open sourced nudity detection model, OpenNSFW. Deep learning models are used to identify the presence of a minor or nudity in any given image or video. The performance of this approach is evaluated on several widely used age estimation and nudity detection datasets. Additionally, preliminary tests were conducted with the help of a local law enforcement agency on a private dataset of SEIC taken from real world cases.

Highlights

  • The proliferation of digital media has led to the most frenzied growth in child pornography than at any other point in history

  • We propose using recent computer vision and machine learning advances to automate the process of identifying Sexually Exploitative Imagery of Children (SEIC) in order to significantly decrease the amount of time law enforcement agents spend on child pornography investigations

  • Even though the performance of the label distribution model is worse in terms of Mean Absolute Error (MAE), it is suspected that the model has learned a better feature representation from which more accurate models may be fine-tuned because of the explicit ordinal relationship introduced when training for label distributions

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Summary

Introduction

The proliferation of digital media has led to the most frenzied growth in child pornography than at any other point in history. When a large number of files are present on the suspect’s hard drive, the agent reviewing the case may become overwhelmed with imagery falsely flagged as being SEIC, especially when many pornographic images are present This is because pornographic content is difficult to distinguish from SEIC using traditional techniques since the notion of age is not explicitly modeled. A novel end-to-end framework, based on learning label distributions, that leverages the availability of human guesses in the APPA-Real dataset for modeling the apparent age estimation problem;. In contrast to most other works on SEIC content detection, we rigorously evaluate our models on a series of challenging datasets to analyze their performance before presenting results on data collected from real law enforcement cases. Additional figures highlighting learned probability distributions are given in the Appendix

CHAPTER 2 Related Work
Age Estimation
Biological Age Estimation
Apparent Age Estimation
Deep Learning
Convolutional Neural Networks
Label Distribution Learning
Automated Pornography Detection
Automated SEIC
Modeling Human Guesses
Normal Distributions
Kernel Density Estimation
Evaluation Measure To evaluate the performance of all models, the Mean Absolute Error (MAE)
Transfer Learning
Apparent Age Estimation Experiments
Datasets
Training Details
Results and Analysis
Age Estimation and Label Distribution Learning
Integrating Deep Learning Models
Performing Inference
Evaluation of Age Estimation and Nudity Detection Models on SFW and NSFW Images
Racial Bias
Challenging Images
Evaluation of Age Estimation Models on Videos
Classification of SEIC Videos and Images
SEIC Image Detection
NSFW Video Detection In
Interpretability (Sample Report/Qualitative Analysis)
Practical Limitations and Considerations
Conclusion

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