A computer-implemented method for detecting digital or physical tampering of an imaged physical credential, the method being executed using one or more processors and comprising: receiving a digital image representing a physical credential for identity certification, wherein the physical credential includes one or more physically embedded security features, wherein the digital image has a plurality of high value regions that include identification data, and wherein at least one of the high value regions is not overlaid with embedded security features, the digital image comprising an array of pixels; processing the digital image with a tamper detector to generate an output corresponding to an intrinsic pixel feature of the digital image and not to data encoded in the embedded security features, wherein the tamper detector is configured to perform a pixel-level analysis on each of the plurality of high value regions of the digital image with respect to a predetermined tampering signature of a specific type of digital and/or physical tampering, wherein the pixel-level analysis is agnostic to the embedded security features in the physical credential, wherein the pixel-level analysis includes an examination of a combination of multiple intrinsic pixel features including one or more pixel features that evidence physical and/or digital tampering and one or more benign features, wherein the combination of intrinsic features include one or more of image capture lighting, an image capture device, optical lensing, an imaging environment, hardware and/or software quantization, or an automatic or manual post imaging enhancement, and wherein the tamper detector comprises a predictive model trained by a machine learning algorithm applying a training dataset, wherein the predictive model includes a probabilistic classifier; and determining, based on the output from the tamper detector, whether the digital image has been digitally tampered with, wherein the output includes a classification of the digital image and a degree of certainty, and wherein determining whether the digital image has been tampered with comprises comparing the degree of certainty to a predetermined threshold. receiving a digital image representing a physical credential for identity certification, wherein the physical credential includes one or more physically embedded security features, wherein the digital image has a plurality of high value regions that include identification data, and wherein at least one of the high value regions is not overlaid with embedded security features, the digital image comprising an array of pixels; processing the digital image with a tamper detector to generate an output corresponding to an intrinsic pixel feature of the digital image and not to data encoded in the embedded security features, wherein the tamper detector is configured to perform a pixel-level analysis on each of the plurality of high value regions of the digital image with respect to a predetermined tampering signature of a specific type of digital and/or physical tampering, wherein the pixel-level analysis is agnostic to the embedded security features in the physical credential, wherein the pixel-level analysis includes an examination of a combination of multiple intrinsic pixel features including one or more pixel features that evidence physical and/or digital tampering and one or more benign features, wherein the combination of intrinsic features include one or more of image capture lighting, an image capture device, optical lensing, an imaging environment, hardware and/or software quantization, or an automatic or manual post imaging enhancement, and wherein the tamper detector comprises a predictive model trained by a machine learning algorithm applying a training dataset, wherein the predictive model includes a probabilistic classifier; and determining, based on the output from the tamper detector, whether the digital image has been digitally tampered with, wherein the output includes a classification of the digital image and a degree of certainty, and wherein determining whether the digital image has been tampered with comprises comparing the degree of certainty to a predetermined threshold.