Recent results of forgery detection by implementing biometric signature verification methods are promising. At present, forensic signature verification in daily casework is performed through visual examination by trained forensic handwriting experts, without reliance on computerassisted methods. With this competition on on-and offline skilled forgery detection, our objective is to make a first step towards bridging the gap between automated biometric performances and expert-based visual comparisons. We intent to combine realistic forensic casework with automated methods by testing systems on a forensic-like new dataset. The results achieved by the participating systems are promising: 2.85% Equal Error Rate (EER) on the online data and 9.15% on the offline data. From these results we indicate that automated methods might be able to support forensic handwriting experts (FHEs) to formulate the strength of evidence that needs to be reported in court in the future.