With the advent of recent technologies, image editing has become accessible even without expertise. However, this ease of manipulation has given rise to malicious manipulation of images, resulting in the creation and dissemination of visually-realistic fake images to spread disinformation online, wrongfully incriminate someone, or commit fraud. The detection of such forgeries is paramount in exposing those deceitful acts. One promising approach involves unveiling the underlying mosaic of an image, which indicates in which colour each pixel was originally sampled prior to demosaicing. As image manipulation will alter the mosaic as well, exposing the mosaic enables the detection and localization of forgeries. The recent introduction of positional learning has facilitated the identification of the image mosaic. Nevertheless, the clues leading to the mosaic are subtle and frail against common operation such as JPEG compression. The pixelwise estimation of the mosaic is thus often imprecise, and a comprehensive analysis and aggregation of the results are necessary to effectively detect and localize forged areas. In this work, we propose mimic: Mosaic Integrity Monitoring for Image a Contrario forensics. an a contrario method to analyse a pixelwise mosaic estimation. We show that despite the weakness of these traces, the sole analysis of mosaic consistency is enough to beat the state of the art in forgery detection and localization on uncompressed images. Moreover, results are promising even on slightly-compressed images. The a contrario framework ensures robustness against false positives, and the complementary nature of mosaic consistency analysis to other forensic tools makes our method highly relevant for detecting forgeries in high-quality images.