Deep　learning　is　a　thriving　field　currently　stuffed　with　many　practical　applications　and　active　research　topics.　It　allows　computers　to　learn　from　experience　and　to　understand　the　world　in　terms　of　a　hierarchy　of　concepts,　with　each　being　defined　through　its　relations　to　simpler　concepts.　Relying　on　the　strong　capabilities　of　deep　learning,　we　propose　a　convolutional　generative　adversarial　network-based　(Conv-GAN)　framework　titled　MalFox,　targeting　adversarial　malware　example　generation　against　third-party　black-box　malware　detectors.　Motivated　by　the　rival　game　between　malware　authors　and　malware　detectors,　MalFox　adopts　a　confrontational　approach　to　produce　perturbation　paths,　with　each　formed　by　up　to　three　methods　(namely　Obfusmal,　Stealmal,　and　Hollowmal)　to　generate　adversarial　malware　examples.　To　demonstrate　the　effectiveness　of　MalFox,　we　collect　a　large　dataset　consisting　of　both　malware　and　benignware　programs,　and　investigate　the　performance　of　MalFox　in　terms　of　accuracy,　detection　rate,　and　evasive　rate　of　the　generated　adversarial　malware　examples.　Our　evaluation　indicates　that　the　accuracy　can　be　as　high　as　99.01%　which　significantly　outperforms　the　other　12　well-known　learning　models.　Furthermore,　the　detection　rate　is　dramatically　decreased　by　45.1%　on　average,　and　the　average　evasive　rate　is　noticeably　improved　by　up　to　56.0%.　IEEE