  As a well-known physical unclonable function that can provide huge number of
challenge response pairs (CRP) with a compact design and fully compatibility
with current electronic fabrication process, the arbiter PUF (APUF) has
attracted great attention. To improve its resilience against modeling attacks,
many APUF variants have been proposed so far. Though the modeling resilience of
response obfuscated APUF variants such as XOR-APUF and lightweight secure APUF
(LSPUF) have been well studied, the challenge obfuscated APUFs (CO-APUFs) such
as feed-forward APUF (FF-APUF), and XOR-FF-APUF are less elucidated,
especially, with the deep learning (DL) methods. This work systematically
evaluates five CO-APUFs including three influential designs of FF-APUF,
XOR-FF-APUF, iPUF, one very recently design and our newly optimized design
(dubbed as OAX-FF-APUF), in terms of their reliability, uniformity (related to
uniqueness), and modeling resilience. Three DL techniques of GRU, TCN and MLP
are employed to examine these CO-APUFs' modeling resilience -- the first two
are newly explored. With computation resource of a common personal computer, we
show that all five CO-APUFs with relatively large scale can be successfully
modeled -- attacking accuracy higher or close to its reliability. The
hyper-parameter tuning of DL technique is crucial for implementing efficient
attacks. Increasing the scale of the CO-APUF is validated to be able to improve
the resilience but should be done with minimizing the reliability degradation.
As the powerful capability of DL technique affirmed by us, we recommend the DL,
specifically the MLP technique always demonstrating best efficacy, to be always
considered for examining the modeling resilience when newly composited APUFs
are devised or to a large extent, other strong PUFs are constructed.
