Recent years have seen a rapid increase in the number of machine learning and signal processing contests. Some of those recently concluded ones include:   

Sponsored from the IEEE Signal Processing Society, there is a Kaggle contest focusing on identifying the camera that took

an image. Finding footage of a crime caught on tape is an investigator's dream. But even with crystal clear, damning evidence,

one critical question always remains–is the footage real? Today, one way to help authenticate footage is to identify the camera

that the image was taken with. Forgeries often require splicing together content from two different cameras. But, unfortunately,

the most common way to do this now is using image metadata, which can be easily falsified itself. However, this is a problem

yet to be sufficiently solved. For this competition, the IEEE Signal Processing Society is challenging you to build an algorithm

that identifies which camera model captured an image by using traces intrinsically left in the image. Helping to solve this

problem would have a big impact on the verification of evidence used in criminal and civil trials and even news reporting.

A currently running Kaggle contest requires participants to classify floating targets as iceberg or not.  Drifting icebergs present

threats to navigation and activities in areas such as offshore of the East Coast of Canada. Currently, many institutions and

companies use aerial reconnaissance and shore-based support to monitor environmental conditions and assess risks from

icebergs. However, in remote areas with particularly harsh weather, these methods are not feasible, and the only viable

monitoring option is via satellite. In this competition, you’re challenged to build an algorithm that automatically identifies if a

remotely sensed target is a ship or iceberg. Improvements made will help drive the costs down for maintaining safe working

conditions.