As　news　reading　on　social　media　becomes　more　and　more　popular,　fake　news　becomes　a　major　issue　concerning　the　public　and　government.　The　fake　news　can　take　advantage　of　multimedia　content　to　mislead　readers　and　get　dissemination,　which　can　cause　negative　effects　or　even　manipulate　the　public　events.　One　of　the　unique　challenges　for　fake　news　detection　on　social　media　is　how　to　identify　fake　news　on　newly　emerged　events.　Unfortunately,　most　of　the　existing　approaches　can　hardly　handle　this　challenge,　since　they　tend　to　learn　event-specific　features　that　can　not　be　transferred　to　unseen　events.　In　order　to　address　this　issue,　we　propose　an　end-to-end　framework　named　Event　Adversarial　Neural　Network　(EANN),　which　can　derive　event-invariant　features　and　thus　benefit　the　detection　of　fake　news　on　newly　arrived　events.　It　consists　of　three　main　components:　the　multi-modal　feature　extractor,　the　fake　news　detector,　and　the　event　discriminator.　The　multi-modal　feature　extractor　is　responsible　for　extracting　the　textual　and　visual　features　from　posts.　It　cooperates　with　the　fake　news　detector　to　learn　the　discriminable　representation　for　the　detection　of　fake　news.　The　role　of　event　discriminator　is　to　remove　the　event-specific　features　and　keep　shared　features　among　events.　Extensive　experiments　are　conducted　on　multimedia　datasets　collected　from　Weibo　and　Twitter.　The　experimental　results　show　our　proposed　EANN　model　can　outperform　the　state-of-the-art　methods,　and　learn　transferable　feature　representations.