We consider the problem of identifying the densest k-node subgraph in a given graph. We write this problem as an instance of rank-constrained cardinality minimization and then relax using the nuclear and l1 norms. Although the original combinatorial problem is NP-hard, we show that the densest k-subgraph can be recovered from the solution of our convex relaxation for certain program inputs. In particular, we establish exact recovery in the case that the input graph contains a single planted clique plus noise in the form of corrupted adjacency relationships. We consider two constructions for this noise. In the first, noise is introduced by an adversary deterministically deleting edges within the planted clique and placing diversionary edges. In the second, these edge corruptions are performed at random. Analogous recovery guarantees for identifying the densest subgraph of fixed size in a bipartite graph are also established, and results of numerical simulations for randomly generated graphs are included to demonstrate the efficacy of our algorithm.