This paper presents a survey of algorithms used to create deepfakes and, more importantly, methods proposed to detect deepfakes in the literature to date. The proposal of technologies that can automatically detect and assess the integrity of digital visual media is therefore indispensable. Deepfake algorithms can create fake images and videos that humans cannot distinguish them from authentic ones. One of those deep learning-powered applications recently emerged is deepfake. Deep learning advances however have also been employed to create software that can cause threats to privacy, democracy and national security. FFAFuzz also achieved higher code coverage by 14.53% on average compared to WEIZZ.ĭeep learning has been successfully applied to solve various complex problems ranging from big data analytics to computer vision and human-level control. The evaluation results showed that FFAFuzz reduced the average time overhead by 76.49% while identifying more completely compared with Redqueen and by 89.10% compared with WEIZZ. We implemented a fast format-aware fuzzing prototype, FFAFuzz, based on our method and evaluated FFAFuzz in real-world structured input applications. Our approach has the following advantages compared to existing works: (1) recognizing I2S dependencies more completely and swiftly using the input based on the de Bruijn sequence and its mapping structure (2) obtaining indirect dependencies with a light dependency existence analysis on the input fragments. We divided the dependencies into Input-to-State (I2S) and indirect dependencies. In this paper, for structured input applications, we propose a fast format-aware fuzzing approach to recognize dependencies from the specified input to the corresponding comparison instruction. Without seeds that fit the input format, existing runtime dependency recognition strategies are limited by incompleteness and high overhead. Fuzzing is one of the most successful software testing techniques used to discover vulnerabilities in programs.
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