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Minute pixel changes in an image drastically change the prediction that thedeep learning model makes. One of the most significant problems that couldarise due to this, for instance, is autonomous driving. Many methods have beenproposed to combat this with varying amounts of success. We propose a 3 stepmethod for defending such attacks. First, we denoise the image usingstatistical methods. Second, we show that adopting multiple color spaces in thesame model can help us to fight these adversarial attacks further as each colorspace detects certain features explicit to itself. Finally, the feature mapsgenerated are enlarged and sent back as an input to obtain even smallerfeatures. We show that the proposed model does not need to be trained to defendan particular type of attack and is inherently more robust to black-box,white-box, and grey-box adversarial attack techniques. In particular, the modelis 56.12 percent more robust than compared models in case of white box attackswhen the models are not subject to adversarial example training.

Using an ensemble color space model to tackle adversarial examples