  Recently, multi-agent collaborative (MAC) perception has been proposed and
outperformed the traditional single-agent perception in many applications, such
as autonomous driving. However, MAC perception is more vulnerable to
adversarial attacks than single-agent perception due to the information
exchange. The attacker can easily degrade the performance of a victim agent by
sending harmful information from a malicious agent nearby. In this paper, we
extend adversarial attacks to an important perception task -- MAC object
detection, where generic defenses such as adversarial training are no longer
effective against these attacks. More importantly, we propose Malicious Agent
Detection (MADE), a reactive defense specific to MAC perception that can be
deployed by each agent to accurately detect and then remove any potential
malicious agent in its local collaboration network. In particular, MADE
inspects each agent in the network independently using a semi-supervised
anomaly detector based on a double-hypothesis test with the Benjamini-Hochberg
procedure to control the false positive rate of the inference. For the two
hypothesis tests, we propose a match loss statistic and a collaborative
reconstruction loss statistic, respectively, both based on the consistency
between the agent to be inspected and the ego agent where our detector is
deployed. We conduct comprehensive evaluations on a benchmark 3D dataset
V2X-sim and a real-road dataset DAIR-V2X and show that with the protection of
MADE, the drops in the average precision compared with the best-case "oracle"
defender against our attack are merely 1.28% and 0.34%, respectively, much
lower than 8.92% and 10.00% for adversarial training, respectively.
