The proposed scheme DeepQShield is quantum-resistant because it incorporates the executions of post cryptography algorithms and is trained and tested on the Deepfake Detection Challenge dataset (DFDC). On the DFDC database it achieved significantly higher accuracy of 99.28% and an impressive AUC value of 0.9997. When compared to the existing systems such as EfficientNet-B7 (accuracy: 97.2% on DFDC), Vision Transformers (ViT) (90 to 98% on Celeb-DF and DFDC), Multi-attentional CNN-LSTM networks (98.2% on DFDC), FuzzyDFD (accuracy: 99% FF++ and 93% on (Celeb-DF). DeepQShield outshines the conventional models in terms of security, scalability, accuracy and robustness making it best suitable for various applications in real-world scenarios like face forensics, social media data authentication.