Anti-spoofing and deepfake identification are prone to errors and attacks. For example, one model of attentional adversarial network to generate fake images that match the original pictures in terms of features, face strength, and semantic information. One drawback of such an adversarial network model is it analyzes only one attack target; However, research is being done on using various models to target multiple attacks. Some other shortcomings of anti-spoofing techniques include failure to detect spoofing across databases, failure to apply to real life scenarios, and performance issues related to the limits of the technologies involved.
