MalJPEG: Machine learning based solution for the detection of malicious JPEG images

Aviad Cohen, Nir Nissim, Yuval Elovici

IEEE Access 8, 19997-20011, 2020

In recent years, cyber-attacks against individuals, businesses, and organizations have increased. Cyber criminals are always looking for effective vectors to deliver malware to victims in order to launch an attack. Images are used on a daily basis by millions of people around the world, and most users consider images to be safe for use; however, some types of images can contain a malicious payload and perform harmful actions. JPEG is the most popular image format, primarily due to its lossy compression. It is used by almost everyone, from individuals to large organizations, and can be found on almost every device (on digital cameras and smartphones, websites, social media, etc.). Because of their harmless reputation, massive use, and high potential for misuse, JPEG images are used by cyber criminals as an attack vector. While machine learning methods have been shown to be effective at detecting known and …