# Deterring Deepfake Attacks with an Electrical Network Frequency Fingerprints Approach
## 1 Introduction  
Modern Artificial Intelligence (AI)/Machine Learning (ML) technology is widely integrated with many multimedia applications to help enhance its applications, and General Adversarial Networks (GANs) enable the manipulation of audio or video streams seamlessly based on the probability distribution of each dataset class [1]. Since first introduced in 2015, the development of the generator and the discriminator module of the GAN has led to the generation of deepfaked images that are indistinguishable from real images [2]. Such high-resolution and accurate generation of images had found many applications in modern media. The potential applications of deepfakes include e-health/medical field, commercial applications, and secure privacy in media. With the capability to generate featurecharacteristics based on a learned probability distribution, a deepfake generation model was proposed to help physically challenged people with entertainment media, where the model extracts motion features from a source subject and generates similar movements using the targeted subject [3]. In medical applications, deepfakes are readily applicable to develop better plastic surgery procedures for facial reconstruction [4]. Along with a guidance-based AI system in surgery, deepfakes are also used to generate training samples for rare medical conditions where the data are limited [4]. Commercial companies develop deepfake techniques to translate text-based messages delivered by artificial or deepfake characters, and similar applications are seen in social media platforms to create online avatars [5]. With the emergence of the metaverse, online deepfake avatars are created to represent virtual presence. Holographic technologies leverage deepfakes to generate 3D historical characters using accurate audio and video data and deliver their story for future generations. Lastly, deepfake applications in privacy preservation stand on a fragile line. One such application includes preserving victims' identities appearing on media platforms by altering their visual and audio characteristics [6]. However, deepfaked video, audio, or photos also can be highly disturbing and able to mislead the public, raising further challenges in policy, technology, social, and legal aspects [7; 8]. Currently, there are deepfake tools available in the public domain that allow people to impersonate anyone, from businessmen to music stars, during video chats [9; 10; 11]. Deepfake video \"attacks\" on some public scenarios have raised serious concerns [12; 13]. Political leaders' messages are altered to create fake news for the public and lower trust in broadcast messages [14]. Researchers have pointed out that disinformation may actually cause societal disturbance and ruin the foundation of trust [15; 16; 17; 18]. For instance, the most recent case was on March 17: a deepfaked video was posted on social media showing that President [PERSON] was calling the Ukraine soldiers to lay down their arms [19; 20]. Domains such as smart surveillance, which highly depends on the audio and the visual layer input for its functionality, could lose the track of malicious actions when the incoming frames are altered [3]. Government agencies such as the U.S. Defense Advanced Research Projects Agency (DARPA) are concerned about losing the war against deepfake attacks from adversarial hackers that use popular ML techniques to automatically incorporate artificial components into existing video streams [21; 22]. Therefore, as a primary cause of misinformation, an imminent need for fast and reliable authentication techniques is of a high priority [14; 23]. While the community has been engaging in the endless AI arms race \"fighting fire with fire\" hoping to have \"smarter\" ML algorithms [24; 25; 26], new ML algorithms keep making fake AVS data more real. Therefore, it is compelling to explore alternative ML deepfake detection solutions.