While　widely　adopted　in　practical　applications,　face　recognition　has　been　critically　discussed　regarding　the　malicious　use　of　face　images　and　the　potential　privacy　problems,　e.g.,　deceiving　payment　system　and　causing　personal　sabotage.　Online　photo　sharing　services　unintentionally　act　as　the　main　repository　for　malicious　crawler　and　face　recognition　applications.　This　work　aims　to　develop　a　privacy-preserving　solution,　called　Adversarial　Privacy-preserving　Filter　(AIM,　to　protect　the　online　shared　face　images　from　being　maliciously　used.　We　propose　an　end-cloud　collaborated　adversarial　attack　solution　to　satisfy　requirements　of　privacy,　utility　and　non-accessibility.　Specifically,　the　solutions　consist　of　three　modules:　(1)　image-specific　gradient　generation,　to　extract　image-specific　gradient　in　the　user　end　with　a　compressed　probe　model;　(2)　adversarial　gradient　transfer,　to　fine-tune　the　image-specific　gradient　in　the　server　cloud;　and　(3)　universal　adversarial　perturbation　enhancement,　to　append　image-independent　perturbation　to　derive　the　final　adversarial　noise.　Extensive　experiments　on　three　datasets　validate　the　effectiveness　and　efficiency　of　the　proposed　solution.　A　prototype　application　is　also　released　for　further　evaluation.　We　hope　the　end-cloud　collaborated　attack　framework　could　shed　light　on　addressing　the　issue　of　online　multimedia　sharing　privacy-preserving　issues　from　user　side.(1)