  Deep learning solutions in critical domains like autonomous vehicles, facial
recognition, and sentiment analysis require caution due to the severe
consequences of errors. Research shows these models are vulnerable to
adversarial attacks, such as data poisoning and neural trojaning, which can
covertly manipulate model behavior, compromising reliability and safety.
Current defense strategies like watermarking have limitations: they fail to
detect all model modifications and primarily focus on attacks on CNNs in the
image domain, neglecting other critical architectures like RNNs.
  To address these gaps, we introduce DeepiSign-G, a versatile watermarking
approach designed for comprehensive verification of leading DNN architectures,
including CNNs and RNNs. DeepiSign-G enhances model security by embedding an
invisible watermark within the Walsh-Hadamard transform coefficients of the
model's parameters. This watermark is highly sensitive and fragile, ensuring
prompt detection of any modifications. Unlike traditional hashing techniques,
DeepiSign-G allows substantial metadata incorporation directly within the
model, enabling detailed, self-contained tracking and verification.
  We demonstrate DeepiSign-G's applicability across various architectures,
including CNN models (VGG, ResNets, DenseNet) and RNNs (Text sentiment
classifier). We experiment with four popular datasets: VGG Face, CIFAR10, GTSRB
Traffic Sign, and Large Movie Review. We also evaluate DeepiSign-G under five
potential attacks. Our comprehensive evaluation confirms that DeepiSign-G
effectively detects these attacks without compromising CNN and RNN model
performance, highlighting its efficacy as a robust security measure for deep
learning applications. Detection of integrity breaches is nearly perfect, while
hiding only a bit in approximately 1% of the Walsh-Hadamard coefficients.
