Facial recognition-based biometric authentication is increasingly frequently employed. However, a facial recognition system should not only recognize an individual's face, but it should also be capable of detecting spoofing attempts using printed faces or digital photographs. There are now various methods for detecting spoofing, including blinking, lip movement, and head tilt detection. However, this approach has limitations when dealing with dynamic video spoofing assaults. On the other hand, these types of motion detection systems can diminish user comfort. As a result, this article presents a method for identifying facial spoofing attacks through Convolutional Neural Networks. The anti-spoofing technique is intended to be used in conjunction with deep learning models without using extra tools or equipment. Our CNN classification dataset can be derived from the NUAA photo imposter and CASIA v2. The collection contains numerous examples of facial spoofing, including those created with posters, masks, and smartphones. In the pre-processing stage, image augmentation is carried out with brightness adjustments and other filters so that the model to adapt to various environmental conditions. We evaluate the number of epochs, optimizer types, and the learning rate during the testing process. The test results show that the proposed model achieves an accuracy value of 91.23% and an F1 score of 92.01%.