# Face Morphing, a Modern Threat to Border Security: Recent Advances and Open Challenges
## 7 Discussion  
Morphing attacks constitute a well-established and pertinent security issue. The bibliography regarding both morphing algorithms and morphing attack detection, is already in a mature stage, as it is clear from the works presented in this paper. However, despite the progress being made, the detection of morphing attacks remains still an open and challenging issue, both because of the difficulty posed by the problem itself - morphed images can become so realistic that even trained experts perform poorly in their detection [10; 11]--but also, due to the fact that the attempts to solve it are hindered by the lack of standardized procedures for creating morphed images, and of benchmarks data, in general.  
Mostly, the proposed detection techniques are trained and tested using self-created datasets, meaning that the presented results are hard to reproduce and difficult to interpret. This issue is reported widely in the bibliography, with [9; 52] being the most recent examples. Due to this reason, explicit performance comparisons between rival MAD approaches are left out of this paper, and the interested reader is referred to relevant works included in the bibliography, such as [6; 52].  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{2}{c}{**On-Line**} & \multicolumn{2}{c}{**Off-Line**} \\ \hline
**Deep** & **Non-Deep** & **Deep** & **Non-Deep** \\ \hline [PERSON] et al. (2019) [72] & [PERSON] et al. (2017) [62] & [PERSON] et al. (2017) [22] & [PERSON] et al. (2019) [65] \\ [PERSON] et al (2020) [73] & [PERSON] et al. (2020) [64] & [PERSON] et al. (2020) [44] & [PERSON] et al. (2018) [66] \\ [PERSON] et al. (2020) [50] & & [PERSON] (2017) [67] & [PERSON] et al. (2018) [37] \\  & & [PERSON] et al (2019) [75] & [PERSON] (2016) [69] \\  & & [PERSON] et al (2020) [76] & [PERSON] et al. (2017) [70] \\  & & [PERSON] et al. (2017) [71] \\  & & [PERSON] et al. (2019) [9] \\ \hline \hline \end{tabular}
\end{table}
Table 1: Categorization of selected works on Morphing Attack Detection.  
Constructing a benchmark dataset of morphed images is a necessary step, albeit a time-consuming and a painstaking one. It should be also noted that automatically produced face morphs may not represent a realistic attack scenario. After all, a morphing attack represents a scenario where the attacker has ample time and resources to produce a \"single\" good image that will \"fool\" the FRS. The dataset needs to be as true-to-life as possible, and as extensive/representative as possible.  
The previous remark is especially important for utilizing the full potential of deep networks for the problem of automatic morphing detection. The true power of deep architectures lies in the automatic learning of features, which is only made possible by the availability of extensive training datasets. To avoid over-fitting, apart from training data, new, specialized architectures and/or training protocols, tailored to the specific needs of the morphing detection problem, are required, as noted in the relevant bibliography [44]. Along the same lines, regarding the non-deep classification approach, new specialized descriptors/features must be devised and used instead of (or in conjunction with) general-purpose descriptors such as LBP or BSIF that are devised for Computer Vision tasks, which are very different in nature to morphing detection.  
Finally, the need for independent evaluation of the algorithms, using dedicated platforms and testing datasets is also an issue raised in the bibliography with attempts being already made in this direction [52; 79].