Recent　research　on　vision-based　tasks　has　achieved　great　improvement　due　to　the　development　of　deep　learning　solutions.　However,　deep　models　have　been　found　vulnerable　to　adversarial　attacks　where　the　original　inputs　are　maliciously　manipulated　and　cause　dramatic　shifts　to　the　outputs.　In　this　paper,　we　focus　on　adversarial　attacks　in　image　classifiers　built　with　deep　neural　networks　and　propose　a　model-agnostic　approach　to　detect　adversarial　inputs.　We　argue　that　the　logit　semantics　of　adversarial　inputs　follow　a　different　evolution　with　respect　to　original　inputs,　and　construct　a　logits-based　embedding　of　features　for　effective　representation　learning.　We　train　an　LSTM　network　to　further　analyze　the　sequence　of　logits-based　features　to　detect　adversarial　examples.　Experimental　results　on　the　MNIST,　CIFAR-10,　and　CIFAR-100　datasets　show　that　our　method　achieves　state-of-the-art　accuracy　for　detecting　adversarial　examples　and　has　strong　generalizability.　©　2021　IEEE.