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The rapid expansion of the digital world introduces complex challenges within the forensic and security domains. In particular, the wide availability of online pornographic media is a huge problem for applications that seek to prevent exposure to inappropriate/undesired audiences, or that aim to automate the detection of any illegal behavior. There is a thin veil separating the definition of pornographic and non-pornographic media, making it difficult, even for humans, to agree on a consistent interpretation. Most of the available APIs for detecting NSFW (not-safe-for-work) media are not able to infer clearly whether a file contains pornographic content or not. In general, given an input file, APIs return a set of probability scores, leaving the responsibility of a final binary decision to the users. What is more, NSFW APIs do not publicly share their training datasets. Aiming to mitigate these issues, we introduce a novel dataset …