Deep neural networks (DNNs), while accurate, are expensive to train. Many
practitioners, therefore, outsource the training process to third parties or
use pre-trained DNNs. This practice makes DNNs vulnerable to $backdoor$
$attacks$: the third party who trains the model may act maliciously to inject
hidden behaviors into the otherwise accurate model. Until now, the mechanism to
inject backdoors has been limited to $poisoning$.

We argue that such a supply-chain attacker has more attack techniques
available. To study this hypothesis, we introduce a handcrafted attack that
directly manipulates the parameters of a pre-trained model to inject backdoors.
Our handcrafted attacker has more degrees of freedom in manipulating model
parameters than poisoning. This makes it difficult for a defender to identify
or remove the manipulations with straightforward methods, such as statistical
analysis, adding random noises to model parameters, or clipping their values
within a certain range. Further, our attacker can combine the handcrafting
process with additional techniques, $e.g.$, jointly optimizing a trigger
pattern, to inject backdoors into complex networks effectively$-$the
meet-in-the-middle attack.

In evaluations, our handcrafted backdoors remain effective across four
datasets and four network architectures with a success rate above 96%. Our
backdoored models are resilient to both parameter-level backdoor removal
techniques and can evade existing defenses by slightly changing the backdoor
attack configurations. Moreover, we demonstrate the feasibility of suppressing
unwanted behaviors otherwise caused by poisoning. Our results suggest that
further research is needed for understanding the complete space of supply-chain
backdoor attacks.

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