LBP-LSB Co-Optimisation for Dynamic Unseen Backdoor Attacks

Aiming at the problems of fixed trigger patterns that are prone to detection in existing invisible backdoor attacks, this paper proposes a backdoor attack method that integrates local binary pattern (LBP) with dynamic randomized least significant bit (LSB) steganography. The multi-bit coding characteristic of LBP is leveraged to enrich the representational expressiveness of trigger information within the embedding budget, combined with LSB steganography to maintain visual imperceptibility, and a pseudo-random number generator (PRNG) is introduced to randomize embedding locations to mitigate detectors that rely on fixed-position patterns. Experiments show that the proposed method demonstrates potential advantages in terms of steganography, attack success rate, and anti-detection capability on both CIFAR-10 and Tiny-ImageNet datasets. Among them, the structural similarity index (SSIM) and peak signal-to-noise ratio (PSNR) reach up to 0.98 and above 36 dB in terms of covertness, respectively. In anti-detection experiments, the attack method maintains high attack success rates under D-BR defense (CIFAR-10: Test_ASR > 85%; Tiny-ImageNet: Test_ASR > 95%), while under SPECTRE defense—a spectral-based statistical method—the defender’s leakage detection rate of poisoned samples remains low (CIFAR-10: 5.96%; Tiny-ImageNet: 10.56%). This clearly validates the proposed attack’s robustness against mainstream defense mechanisms.