In this paper, we tackle the problem of detecting the so-called Adobe pattern. Recent research showed that RAW and 16-bit images developed with the Lightroom or CameraRaw software into 8-bit formats are modified by an imperceptible periodical pattern. This 128x128 pattern is influenced by the 16-bit valued content and is incorporated in the 16-bit domain, making it impossible to estimate perfectly from real 8-bit images. Furthermore, as this periodic pattern can be perceived as a bias shared among different users and camera models, it has led to inaccurate camera attribution when working with the Photo-Response Non-Uniformity (PRNU). To effectively eliminate this bias, it is therefore imperative to have an accurate method of detecting the Adobe pattern. We model the content-dependent Adobe pattern as a deterministic pattern corrupted by uniform noise, which enables us to frame the detection of the Adobe pattern as a hypothesis test. Using the Likelihood Ratio Test, we demonstrate that for images without the Adobe pattern, a meticulously designed test statistic follows a zero-mean Gaussian distribution with a constant variance. Moreover, the detection accuracy exceeds 90% at false positive rate of 10^{-4} for 128x128 images JPEG compressed with quality 80, and improves with higher image quality. Finally, we find that around 16% of images in the FFHQ dataset of real faces contain the Adobe pattern.