Image-based object detection and classification are essential for satellite-based monitoring, which spans multiple safety-critical engineering applications. Meanwhile, state-of-the-art deep learning significant improves the accuracy for image classification tasks thus has been deployed in various scenarios. However, it's well-known deep learning-based image classifiers are vulnerable to small perturbations along specific directions, known as adversarial attacks. These attacks are exceptionally effective to fool image classifiers. In extreme cases, merely one pixel's change can lead to a attacker-desired wrong prediction label. In this work, we show that deep learning with Bayesian formulation can extend the deep learning adversarial robustness by a large margin, without the need of adversarial training. Moreover, we show that the stochastic classifier after the deterministic CNN extractor has sufficient robustness enhancement rather than a stochastic feature extractor before the stochastic classifier. This advises on utilizing stochastic layers in building decision-making pipelines within a safety-critical domain. Additionally, we show that the Bayesian posterior can act as the safety precursor of ongoing malicious activities towards a deployed image classification system. This leads to the detection of adversarial samples in cybersecurity. With lots of potentials, we leave them as the future studies.