Nowadays with the growth of social media, users upload millions of photos in different platforms online. Researchers in the field of computer vision devote their time and effort to analyze images in order to gain valuable insight. Data analysis and classification can be impeded by different factors. One of which is the image filters that are studied in this work. People greatly change the appearance of their photos by adding filters in order to make them more appealing. Instagram is arguably one of the most popular social media platforms online. With the platform’s growth, filtering images has also become more popular. In this thesis a subset of Instagram filters has been selected in order to study their impact with a series of experiments. To our knowledge, no mention has been made of image filters’ impact in prior work, in the domain of machine classification and human perception. Image filters can create many challenges depending on the application they are used in. In this thesis, focus has been given on classification of weather conditions. Systems have been designed to receive images and accurately identify the weather conditions that exist in them solely using visual features and no prior knowledge. In weather forecasting a lot of resources are spent in order to study past and current weather conditions so as to predict the state of the weather in the future. Gathering and documenting weather related information can be aided by these aforementioned systems. However, if researchers would like to use social images to extract insight, they need to change their approach accordingly. As it is documented in the following chapters, dealing with these photos can be problematic and can cause huge decline in performance. For this reason, the algorithmic design has been changed by using different techniques inspired from the domain of Adversarial Machine Learning to measure their effect. In addition to machine classification, filters can influence human perception as well. A study is conducted that measures the impact filters have on the ability of humans identifying the weather conditions in images. From the quantitative and qualitative analysis of the results several key findings are extracted regarding the effect of filters and the visual cues that are used by people. People have identified certain visual cues that have not been encoded in the classifier such as the type of clothing people are wearing. Instead much simpler features have been engineered and the performance of the classifier is still quite high.