  According to the 2020 cyber threat defence report, 78% of Canadian
organizations experienced at least one successful cyberattack in 2020. The
consequences of such attacks vary from privacy compromises to immersing damage
costs for individuals, companies, and countries. Specialists predict that the
global loss from cybercrime will reach 10.5 trillion US dollars annually by
2025. Given such alarming statistics, the need to prevent and predict
cyberattacks is as high as ever. Our increasing reliance on Machine
Learning(ML)-based systems raises serious concerns about the security and
safety of these systems. Especially the emergence of powerful ML techniques to
generate fake visual, textual, or audio content with a high potential to
deceive humans raised serious ethical concerns. These artificially crafted
deceiving videos, images, audio, or texts are known as Deepfakes garnered
attention for their potential use in creating fake news, hoaxes, revenge porn,
and financial fraud. Diversity and the widespread of deepfakes made their
timely detection a significant challenge. In this paper, we first offer
background information and a review of previous works on the detection and
deterrence of deepfakes. Afterward, we offer a solution that is capable of 1)
making our AI systems robust against deepfakes during development and
deployment phases; 2) detecting video, image, audio, and textual deepfakes; 3)
identifying deepfakes that bypass detection (deepfake hunting); 4) leveraging
available intelligence for timely identification of deepfake campaigns launched
by state-sponsored hacking teams; 5) conducting in-depth forensic analysis of
identified deepfake payloads. Our solution would address important elements of
the Canada National Cyber Security Action Plan(2019-2024) in increasing the
trustworthiness of our critical services.
