Our reliance on the face as a means of identification is likely a result of our belief that we are face experts. For example, face photographs are featured in many forms of documentation internationally, including passports and driving licenses. The use of biometrics in identification is commonplace across a variety of contexts. Importantly, we found that a simple computer model performed better than our human participants, suggesting that security agencies should focus on automated solutions rather than training people when fighting morphing attacks. When morphs were compared to faces during a live interaction, they were accepted at concerning levels and, again, detection was error-prone. We found that on-screen morph detection was poor and training did not lead to improvements. Here, we reconsidered these findings with the use of higher-quality morphs, where every effort was made to produce images comparable with those we expect criminals to use. Recent research has begun to investigate whether people can detect morphs and has suggested that training might provide an effective way to increase performance. If both people sufficiently resemble the morph, they could both use the resulting genuine passport for international travel. One method used by fraudsters is to submit a morph image (a 50/50 average of two people’s faces) for inclusion in an official document like a passport. As the name suggests, these involve deception during the application process in order to obtain a genuine document, equipped with all the necessary watermarks, and so on. With an increase in the detection of fraudulent IDs, security officers have recently seen a rise in the use of fraudulently obtained genuine (FOG) documents. In order to minimize the use of fraudulent documents as forms of identification, anti-counterfeit measures such as watermarks are often included. Our findings have important implications for security authorities worldwide. Taken together, these results reinforce the idea that advanced computational techniques could prove more reliable than training people when fighting these types of morphing attacks. Finally, we found that a simple computer model outperformed our human participants.
In a live matching task, morphs were accepted at levels suggesting they represent a significant concern for security agencies and detection was again error-prone. Over four experiments, we found that people were highly error-prone when detecting morphs and that training did not produce improvements. Here, we investigate human and computer performance with high-quality morphs, comparable with those expected to be used by criminals. Limited research with low-quality morphs has shown that human detection rates were poor but that training methods can improve performance. By submitting a morph image (a 50/50 average of two people’s faces) for inclusion in an official document such as a passport, it might be possible that both people sufficiently resemble the morph that they are each able to use the resulting genuine ID document. Discriminator is trying to maximize the understanding of real images so as to distinguish the fake samples.In recent years, fraudsters have begun to use readily accessible digital manipulation techniques in order to carry out face morphing attacks.
Morph age saving image generator#
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