Birds' Eye View: Measuring Behavior and Posture of Chickens as a Metric for Their Well-Being

Chicken well-being is important for ensuring food security and better nutrition for a growing global human population. In this research, we represent behavior and posture as a metric to measure chicken well-being. With the objective of detecting chicken posture and behavior in a pen, we employ two algorithms: Mask R-CNN for instance segmentation and YOLOv4 in combination with ResNet50 for classification. Our results indicate a weighted F1 score of 88.46% for posture and behavior detection using Mask R-CNN and an average of 91% accuracy in behavior detection and 86.5% average accuracy in posture detection using YOLOv4. These experiments are conducted under uncontrolled scenarios for both posture and behavior measurements. These metrics establish a strong foundation to obtain a decent indication of individual and group behaviors and postures. Such outcomes would help improve the overall well-being of the chickens. The dataset used in this research is collected in-house and will be made public after the publication as it would serve as a very useful resource for future research. To the best of our knowledge no other research work has been conducted in this specific setup used for this work involving multiple behaviors and postures simultaneously.

LowKey: Leveraging Adversarial Attacks to Protect Social Media Users from Facial Recognition

Facial recognition systems are increasingly deployed by private corporations, government agencies, and contractors for consumer services and mass surveillance programs alike. These systems are typically built by scraping social media profiles for user images. Adversarial perturbations have been proposed for bypassing facial recognition systems. However, existing methods fail on full-scale systems and commercial APIs. We develop our own adversarial filter that accounts for the entire image processing pipeline and is demonstrably effective against industrial-grade pipelines that include face detection and large scale databases. Additionally, we release an easy-to-use webtool that significantly degrades the accuracy of Amazon Rekognition and the Microsoft Azure Face Recognition API, reducing the accuracy of each to below 1%.