Anomaly detectors address the difficult problem of detecting automatically exceptions in an arbitrary background image. Detection methods have been proposed by the thousands because each problem requires a different background model. By analysing key examples of the literature, we show that all anomaly detectors are characterized by their choice among seven fundamental principles guiding the background model and the decision method. We show that these principles can be combined in a general method that uses six of them. Our synthesis reduces the problem to the easier problem of detecting anomalies in noise. In that way, the varifold background modeling problem is replaced by simpler noise modeling, and allows the calculation of rigorous thresholds based on the a contrario detection theory. Our conclusion is that it is possible to perform automatic anomaly detection even on a single image. (Joint work with Thibaud Ehret, Axel Davy, Jean-Michel Morel, Mauricio Delbracio)