Leveraging modern Artificial Intelligence (AI) technology, Deepfake attacks manipulate audio/video streams (AVS) to mimic any targeted person or scenario. Deepfake attacks are highly disturbing, and the misinformation can mislead the public, raising further challenges in policy, technical, social, and legal aspects. Electrical Network Frequency (ENF) signals embedded in AVS data are promising to be utilized as fingerprints to authenticate digital media and timely detect deepfaked audio or video. Meanwhile, the success of ENF-based deepfake detection approaches will be forfeited if attackers can create false ENF fingerprints to fool the detector. In this paper, a thorough experimental study validates the robustness of ENF signals as a fingerprint for digital media authentication. Taking statistical, supervised learning, and deep learning approaches, this work shows that it is infeasible to forecast the future ENF signals based on historical records. While strict theoretical proof is yet to be done, this work experimentally verifies ENF signals as a reliable fingerprint to authenticate digital media.