  As a critical threat to deep neural networks (DNNs), backdoor attacks can be
categorized into two types, i.e., source-agnostic backdoor attacks (SABAs) and
source-specific backdoor attacks (SSBAs). Compared to traditional SABAs, SSBAs
are more advanced in that they have superior stealthier in bypassing mainstream
countermeasures that are effective against SABAs. Nonetheless, existing SSBAs
suffer from two major limitations. First, they can hardly achieve a good
trade-off between ASR (attack success rate) and FPR (false positive rate).
Besides, they can be effectively detected by the state-of-the-art (SOTA)
countermeasures (e.g., SCAn). To address the limitations above, we propose a
new class of viable source-specific backdoor attacks, coined as CASSOCK. Our
key insight is that trigger designs when creating poisoned data and cover data
in SSBAs play a crucial role in demonstrating a viable source-specific attack,
which has not been considered by existing SSBAs. With this insight, we focus on
trigger transparency and content when crafting triggers for poisoned dataset
where a sample has an attacker-targeted label and cover dataset where a sample
has a ground-truth label. Specifically, we implement $CASSOCK_{Trans}$ and
$CASSOCK_{Cont}$. While both they are orthogonal, they are complementary to
each other, generating a more powerful attack, called $CASSOCK_{Comp}$, with
further improved attack performance and stealthiness. We perform a
comprehensive evaluation of the three $CASSOCK$-based attacks on four popular
datasets and three SOTA defenses. Compared with a representative SSBA as a
baseline ($SSBA_{Base}$), $CASSOCK$-based attacks have significantly advanced
the attack performance, i.e., higher ASR and lower FPR with comparable CDA
(clean data accuracy). Besides, $CASSOCK$-based attacks have effectively
bypassed the SOTA defenses, and $SSBA_{Base}$ cannot.
