Do you know if I\u27m real? An experiment to benchmark human recognition of AI-generated faces

With the development of advanced machine learning techniques, it is now possible to generate fake images that may appear authentic to the naked eye. Realistic faces generated using Generative Adversarial Networks have been the focus of discussion in the media for exactly this reason. This study examined how well people can distinguish between real and generated images. 30 real and 60 generated were gathered and put into a survey. Subjects were shown a random 30 of these faces in random sequence and asked to specify whether or not they thought the faces were real. Based on a statistical analysis, the participants were not able to reliably distinguish between all real and generated images, but real images were correctly distinguished in 81% of cases, where generated images were correctly distinguished in 61% of cases. Some generated images did receive very high scores, with one generated image being classified as real in 100% of the cases

Active Anomaly Detection for Key Item Selection in Process Auditing

Process mining allows auditors to retrieve crucial information about transactions by analysing the process data of a client. We propose an approach that supports the identification of unusual or unexpected transactions, also referred to as exceptions. These exceptions can be selected by auditors as “key items”, meaning the auditors wants to look further into the underlying documentation of the transaction. The approach encodes the traces, assigns an anomaly score to each trace, and uses the domain knowledge of auditors to update the assigned anomaly scores through active anomaly detection. The approach is evaluated with three groups of auditors over three cycles. The results of the evaluation indicate that the approach has the potential to support the decision-making process of auditors. Although auditors still need to make a manual selection of key items, they are able to better substantiate this selection. As such, our research can be seen as a step forward with respect to the usage of anomaly detection and data analysis in process auditing

Active Anomaly Detection for Key Item Selection in Process Auditing

Process mining allows auditors to retrieve crucial information about transactions by analysing the process data of a client. We propose an approach that supports the identification of unusual or unexpected transactions, also referred to as exceptions. These exceptions can be selected by auditors as “key items”, meaning the auditors wants to look further into the underlying documentation of the transaction. The approach encodes the traces, assigns an anomaly score to each trace, and uses the domain knowledge of auditors to update the assigned anomaly scores through active anomaly detection. The approach is evaluated with three groups of auditors over three cycles. The results of the evaluation indicate that the approach has the potential to support the decision-making process of auditors. Although auditors still need to make a manual selection of key items, they are able to better substantiate this selection. As such, our research can be seen as a step forward with respect to the usage of anomaly detection and data analysis in process auditing