3D printing systems have expanded the access to low cost, rapid methods for attaining physical prototypes or products. However, a cyber attack, system error, or operator error on a 3D printing system may result in catastrophic situations, ranging from complete product failure, to small types of defects which weaken the structural integrity of the product, making it unreliable for its intended use. Such defects can be introduced early-on via solid models or through G-codes for printer movements at a later stage. Previous works have studied the use of image classifiers to predict defects in real-time as a print is in progress and also by studying the printed entity once the print is complete. However, a major restriction in the functionality of these methods is the availability of a dataset capturing diverse attacks on printed entities or the printing process. This paper introduces a visual inspection technique that analyzes the amplitude and phase variations of the print head platform arising through induced system manipulations. The method uses an image sequence of a 3D printing process captured via an off the shelf camera to perform an offline multi-scale, multi-orientation decomposition to amplify imperceptible system movements attributable to a change in system parameters. The authors hypothesize that a change in the amplitude envelope and instantaneous phase response as a result of a change in the end effector translational instructions, to be correlated with an AM system compromise. A case study is presented that tests the hypothesis and provides statistical validity in support of the method. The method has the potential to enhance the robustness of cyber-physical systems such as 3D printers that rely on secure, high quality hardware and software to perform optimally.