The fractal dimension and multifractal spectrum are widely used to characterize the complexity of natural fractures. However, a systematic investigation on the impact of different fracture properties (fracture lengths, orientations, center positions, system sizes) on the fractal and multifractal characterization of complex fracture networks is missing. We utilize an in-house developed DFN modeling software, HatchFrac, to construct stochastic fracture networks with prescribed distributions and systematically study the impact of four geometrical properties of fractures on the fractal and multifractal characterization. We calculate the single fractal dimension and multifractal spectrum with the box-counting method. The single fractal dimension, D, and the difference of singularity exponent, ∆α, are used to represent the fractal and multifractal patterns, respectively. We find that fracture lengths, orientations and system sizes have positive correlations with D and ∆α, while the system size has the most significant impact among the four parameters. D is uncorrelated with fracture positions (FD), which means that a single fractal dimension cannot capture the complexity caused by clustering effects. However, ∆α has a strong negative correlation with FD, which implies that clustering effects make fracture networks more complex, and ∆α can capture the difference. We also digitize 60 outcrop maps with a novel fracture detection algorithm and calculate their fractal dimension and multifractal spectrum. We find wide variations of D and ∆α on those outcrop maps, even for outcrops at similar scales. It means that a universal indicator for characterizing fracture networks at different scales or the same scale is almost impossible.