Grasping　force　control　is　important　for　multi-fingered　robotic　hands　to　stabilize　the　grasped　object.　Humans　are　able　to　adjust　their　grasping　force　and　react　quickly　to　instabilities　through　tactile　sensing.　However,　grasping　force　control　through　tactile　sensing　with　robotic　hands　is　still　relatively　unexplored.　In　this　paper,　we　make　use　of　tactile　sensing　for　multi-fingered　robot　hands　to　adjust　the　grasping　force　to　stabilize　unknown　objects　without　prior　knowledge　of　their　shape　or　physical　properties.　In　particular,　an　online　detection　module　based　on　Deep　Neural　Network　(DNN)　is　designed　to　detect　contact　events　and　object　material　simultaneously　from　tactile　data.　In　addition,　a　force　estimation　method　based　on　Gaussian　Mixture　Model　(GMM)　is　proposed　to　compute　the　contact　information　(i.e.,　contact　force　and　contact　location)　from　tactile　data.　According　to　the　results　of　tactile　sensing,　an　object　stabilization　controller　is　then　employed　for　a　robotic　hand　to　adjust　the　contact　configuration　for　object　stabilization.　The　spatio-temporal　property　of　tactile　data　is　exploited　during　tactile　sensing.　Finally,　the　effectiveness　of　the　proposed　framework　is　evaluated　in　a　real-world　experiment　with　a　five-fingered　Shadow　Dexterous　Hand　equipped　with　BioTac　sensors.