  Recently, backdoor attacks pose a new security threat to the training process
of deep neural networks (DNNs). Attackers intend to inject hidden backdoors
into DNNs, such that the attacked model performs well on benign samples,
whereas its prediction will be maliciously changed if hidden backdoors are
activated by the attacker-defined trigger. Existing backdoor attacks usually
adopt the setting that triggers are sample-agnostic, $i.e.,$ different poisoned
samples contain the same trigger, resulting in that the attacks could be easily
mitigated by current backdoor defenses. In this work, we explore a novel attack
paradigm, where backdoor triggers are sample-specific. In our attack, we only
need to modify certain training samples with invisible perturbation, while not
need to manipulate other training components ($e.g.$, training loss, and model
structure) as required in many existing attacks. Specifically, inspired by the
recent advance in DNN-based image steganography, we generate sample-specific
invisible additive noises as backdoor triggers by encoding an
attacker-specified string into benign images through an encoder-decoder
network. The mapping from the string to the target label will be generated when
DNNs are trained on the poisoned dataset. Extensive experiments on benchmark
datasets verify the effectiveness of our method in attacking models with or
without defenses.
